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Статті в журналах з теми "Learned representation"
1
Kalm, Kristjan, and Dennis Norris. "Sequence learning recodes cortical representations instead of strengthening initial ones." PLOS Computational Biology 17, no. 5 (May 24, 2021): e1008969. http://dx.doi.org/10.1371/journal.pcbi.1008969.
Повний текст джерелаАнотація:
We contrast two computational models of sequence learning. The associative learner posits that learning proceeds by strengthening existing association weights. Alternatively, recoding posits that learning creates new and more efficient representations of the learned sequences. Importantly, both models propose that humans act as optimal learners but capture different statistics of the stimuli in their internal model. Furthermore, these models make dissociable predictions as to how learning changes the neural representation of sequences. We tested these predictions by using fMRI to extract neural activity patterns from the dorsal visual processing stream during a sequence recall task. We observed that only the recoding account can explain the similarity of neural activity patterns, suggesting that participants recode the learned sequences using chunks. We show that associative learning can theoretically store only very limited number of overlapping sequences, such as common in ecological working memory tasks, and hence an efficient learner should recode initial sequence representations.
2
Williamson, James R. "How is representation learned?" Behavioral and Brain Sciences 21, no. 4 (August 1998): 484. http://dx.doi.org/10.1017/s0140525x9843125x.
Повний текст джерелаАнотація:
Edelman's memory-based approach to visual representation is preferable to parts-based alternatives. However, the existing algorithms for learning the shape prototypes are biologically implausible because they are nonlocal and nonconstructive. There is an alternative learning algorithm that constructs a mixture model of prototypes on-line, using only local information, and is more biologically plausible and may perform sufficiently well.
3
Yue, Zhihan, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, and Bixiong Xu. "TS2Vec: Towards Universal Representation of Time Series." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 8980–87. http://dx.doi.org/10.1609/aaai.v36i8.20881.
Повний текст джерелаАнотація:
This paper presents TS2Vec, a universal framework for learning representations of time series in an arbitrary semantic level. Unlike existing methods, TS2Vec performs contrastive learning in a hierarchical way over augmented context views, which enables a robust contextual representation for each timestamp. Furthermore, to obtain the representation of an arbitrary sub-sequence in the time series, we can apply a simple aggregation over the representations of corresponding timestamps. We conduct extensive experiments on time series classification tasks to evaluate the quality of time series representations. As a result, TS2Vec achieves significant improvement over existing SOTAs of unsupervised time series representation on 125 UCR datasets and 29 UEA datasets. The learned timestamp-level representations also achieve superior results in time series forecasting and anomaly detection tasks. A linear regression trained on top of the learned representations outperforms previous SOTAs of time series forecasting. Furthermore, we present a simple way to apply the learned representations for unsupervised anomaly detection, which establishes SOTA results in the literature. The source code is publicly available at https://github.com/yuezhihan/ts2vec.
4
Mu, Shanlei, Yaliang Li, Wayne Xin Zhao, Siqing Li, and Ji-Rong Wen. "Knowledge-Guided Disentangled Representation Learning for Recommender Systems." ACM Transactions on Information Systems 40, no. 1 (January 31, 2022): 1–26. http://dx.doi.org/10.1145/3464304.
Повний текст джерелаАнотація:
In recommender systems, it is essential to understand the underlying factors that affect user-item interaction. Recently, several studies have utilized disentangled representation learning to discover such hidden factors from user-item interaction data, which shows promising results. However, without any external guidance signal, the learned disentangled representations lack clear meanings, and are easy to suffer from the data sparsity issue. In light of these challenges, we study how to leverage knowledge graph (KG) to guide the disentangled representation learning in recommender systems. The purpose for incorporating KG is twofold, making the disentangled representations interpretable and resolving data sparsity issue. However, it is not straightforward to incorporate KG for improving disentangled representations, because KG has very different data characteristics compared with user-item interactions. We propose a novel K nowledge-guided D isentangled R epresentations approach ( KDR ) to utilizing KG to guide the disentangled representation learning in recommender systems. The basic idea, is to first learn more interpretable disentangled dimensions (explicit disentangled representations) based on structural KG, and then align implicit disentangled representations learned from user-item interaction with the explicit disentangled representations. We design a novel alignment strategy based on mutual information maximization. It enables the KG information to guide the implicit disentangled representation learning, and such learned disentangled representations will correspond to semantic information derived from KG. Finally, the fused disentangled representations are optimized to improve the recommendation performance. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model in terms of both performance and interpretability.
5
Mel, Bartlett W., and József Fiser. "Minimizing Binding Errors Using Learned Conjunctive Features." Neural Computation 12, no. 4 (April 1, 2000): 731–62. http://dx.doi.org/10.1162/089976600300015574.
Повний текст джерелаАнотація:
We have studied some of the design trade-offs governing visual representations based on spatially invariant conjunctive feature detectors, with an emphasis on the susceptibility of such systems to false-positive recognition errors—Malsburg's classical binding problem. We begin by deriving an analytical model that makes explicit how recognition performance is affected by the number of objects that must be distinguished, the number of features included in the representation, the complexity of individual objects, and the clutter load, that is, the amount of visual material in the field of view in which multiple objects must be simultaneously recognized, independent of pose, and without explicit segmentation. Using the domain of text to model object recognition in cluttered scenes, we show that with corrections for the nonuniform probability and nonindependence of text features, the analytical model achieves good fits to measured recognition rates in simulations involving a wide range of clutter loads, word sizes, and feature counts. We then introduce a greedy algorithm for feature learning, derived from the analytical model, which grows a representation by choosing those conjunctive features that are most likely to distinguish objects from the cluttered backgrounds in which they are embedded. We show that the representations produced by this algorithm are compact, decorrelated, and heavily weighted toward features of low conjunctive order. Our results provide a more quantitative basis for understanding when spatially invariant conjunctive features can support unambiguous perception in multiobject scenes, and lead to several insights regarding the properties of visual representations optimized for specific recognition tasks.
6
Mel, Bartlett W., and József Fiser. "Minimizing Binding Errors Using Learned Conjunctive Features." Neural Computation 12, no. 2 (February 1, 2000): 247–78. http://dx.doi.org/10.1162/089976600300015772.
Повний текст джерелаАнотація:
We have studied some of the design trade-offs governing visual representations based on spatially invariant conjunctive feature detectors, with an emphasis on the susceptibility of such systems to false-positive recognition errors—Malsburg's classical binding problem. We begin by deriving an analytical model that makes explicit how recognition performance is affected by the number of objects that must be distinguished, the number of features included in the representation, the complexity of individual objects, and the clutter load, that is, the amount of visual material in the field of view in which multiple objects must be simultaneously recognized, independent of pose, and without explicit segmentation. Using the domain of text to model object recognition in cluttered scenes, we show that with corrections for the nonuniform probability and nonindependence of text features, the analytical model achieves good fits to measured recognition rates in simulations involving a wide range of clutter loads, word sizes, and feature counts. We then introduce a greedy algorithm for feature learning, derived from the analytical model, which grows a representation by choosing those conjunctive features that are most likely to distinguish objects from the cluttered backgrounds in which they are embedded. We show that the representations produced by this algorithm are compact, decorrelated, and heavily weighted toward features of low conjunctive order. Our results provide a more quantitative basis for understanding when spatially invariant conjunctive features can support unambiguous perception in multiobject scenes, and lead to several insights regarding the properties of visual representations optimized for specific recognition tasks.
7
Sun, Jingyuan, Shaonan Wang, Jiajun Zhang, and Chengqing Zong. "Towards Sentence-Level Brain Decoding with Distributed Representations." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7047–54. http://dx.doi.org/10.1609/aaai.v33i01.33017047.
Повний текст джерелаАнотація:
Decoding human brain activities based on linguistic representations has been actively studied in recent years. However, most previous studies exclusively focus on word-level representations, and little is learned about decoding whole sentences from brain activation patterns. This work is our effort to mend the gap. In this paper, we build decoders to associate brain activities with sentence stimulus via distributed representations, the currently dominant sentence representation approach in natural language processing (NLP). We carry out a systematic evaluation, covering both widely-used baselines and state-of-the-art sentence representation models. We demonstrate how well different types of sentence representations decode the brain activation patterns and give empirical explanations of the performance difference. Moreover, to explore how sentences are neurally represented in the brain, we further compare the sentence representation’s correspondence to different brain areas associated with high-level cognitive functions. We find the supervised structured representation models most accurately probe the language atlas of human brain. To the best of our knowledge, this work is the first comprehensive evaluation of distributed sentence representations for brain decoding. We hope this work can contribute to decoding brain activities with NLP representation models, and understanding how linguistic items are neurally represented.
8
Elio, Renée. "Representation of Similar Well-Learned Cognitive Procedures." Cognitive Science 10, no. 1 (January 1986): 41–73. http://dx.doi.org/10.1207/s15516709cog1001_2.
Повний текст джерела
9
Partarakis, Nikos, Voula Doulgeraki, Effie Karuzaki, George Galanakis, Xenophon Zabulis, Carlo Meghini, Valentina Bartalesi, and Daniele Metilli. "A Web-Based Platform for Traditional Craft Documentation." Multimodal Technologies and Interaction 6, no. 5 (May 10, 2022): 37. http://dx.doi.org/10.3390/mti6050037.
Повний текст джерелаАнотація:
A web-based authoring platform for the representation of traditional crafts is proposed. This platform is rooted in a systematic method for craft representation, the adoption, knowledge, and representation standards of the cultural heritage (CH) domain, and the integration of outcomes from advanced digitization techniques. In this paper, we present the implementation of this method by an online, collaborative documentation platform where digital assets are curated into digitally preservable craft representations. The approach is demonstrated through the representation of three traditional crafts as use cases, and the lessons learned from this endeavor are presented.
10
Wang, Ke, Jiayong Liu, and Jing-Yan Wang. "Learning Domain-Independent Deep Representations by Mutual Information Minimization." Computational Intelligence and Neuroscience 2019 (June 16, 2019): 1–14. http://dx.doi.org/10.1155/2019/9414539.
Повний текст джерелаАнотація:
Domain transfer learning aims to learn common data representations from a source domain and a target domain so that the source domain data can help the classification of the target domain. Conventional transfer representation learning imposes the distributions of source and target domain representations to be similar, which heavily relies on the characterization of the distributions of domains and the distribution matching criteria. In this paper, we proposed a novel framework for domain transfer representation learning. Our motive is to make the learned representations of data points independent from the domains which they belong to. In other words, from an optimal cross-domain representation of a data point, it is difficult to tell which domain it is from. In this way, the learned representations can be generalized to different domains. To measure the dependency between the representations and the corresponding domain which the data points belong to, we propose to use the mutual information between the representations and the domain-belonging indicators. By minimizing such mutual information, we learn the representations which are independent from domains. We build a classwise deep convolutional network model as a representation model and maximize the margin of each data point of the corresponding class, which is defined over the intraclass and interclass neighborhood. To learn the parameters of the model, we construct a unified minimization problem where the margins are maximized while the representation-domain mutual information is minimized. In this way, we learn representations which are not only discriminate but also independent from domains. An iterative algorithm based on the Adam optimization method is proposed to solve the minimization to learn the classwise deep model parameters and the cross-domain representations simultaneously. Extensive experiments over benchmark datasets show its effectiveness and advantage over existing domain transfer learning methods.
Дисертації з теми "Learned representation"
1
Qiao, Xiaomei. "The Representation of Newly Learned Words in the Mental Lexicon." Diss., The University of Arizona, 2009. http://hdl.handle.net/10150/194383.
Повний текст джерелаАнотація:
Most research in word recognition uses words that already exist in the reader's lexicon, and it is therefore of interest to see whether newly learned words are represented and processed in the same way as already known words. For example, are newly learned words immediately represented in a special form of lexical memory, or is there a gradual process of assimilation? As for L2 language learners, are newly learned words incorporated into the same processing system that serves L1, or are they represented quite independently?The current study examines this issue by testing for the existence of the Prime Lexicality Effect (PLE) observed in masked priming experiments (Forster & Veres, 1998). Strong form priming was found with nonword primes (e.g., contrapt-CONTRACT), but not with word primes (e.g., contrast-CONTRACT). This effect is generally assumed to result from competition between the prime and the target. So if the readers had been trained to treat "contrapt" as a new word, would it now function like a word and produce much weaker priming? Elgort (2007) demonstrated such an effect with unmasked primes with L2 bilinguals. The current study investigates the PLE in both L1 and L2 bilinguals under different training conditions. When the training program involves mere familiarization (learning to type the words), a PLE was found with visible primes, but not with masked primes, which suggests that unmasked PLE is not the best indicator of lexicalization. In the case of "real" acquisition where the new word is given a definition and a picture of the object it refers to, and learning is spread over two weeks, a clear PLE was obtained. However, when the same experiment was carried out on Chinese-English bilinguals using the same English materials, completely opposite results were obtained. The learning enhanced priming, rather than reducing it, suggesting that the L2 lexicon might differ qualitatively from the L1 lexicon. The implications of these results for competitive theories of lexical access are discussed, and alternative explanations are considered.
2
Donald, Pauline Sarah Moore. ""Lessons will be learned"? : an investigation into the representation of 'asylum seekers'/refugees in British and Scottish television and impacts on beliefs and behaviours in local communities." Thesis, University of Glasgow, 2011. http://theses.gla.ac.uk/3628/.
Повний текст джерелаАнотація:
This thesis examines media representations and audience reception processes through a detailed study of media reporting and public understandings of asylum and refugee issues. It is based on sixty interviews in which refugees seeking asylum, professionals working with them and members of the general public were invited to comment on their own memories and beliefs using pictures from the TV coverage. The pictures used are included in a detailed thematic content analysis of national and regional broadcast news. Public understandings are systematically compared to the content of media reporting. In particular it explore people’s memories and beliefs of national and regional broadcast news. The content analysis revealed that the national news represents asylum in unsubstantiated and problematic ways whilst the regional news has a more balanced approach to representation of the issue. The thesis explores the diversity of audience reactions and the different ways in which people may accept or reject the media representations. However it also draws attention to the themes which recurred in all of the interviews and argues that there is strong evidence of media effects. The thesis highlights factors in media coverage which are particularly influential. It demonstrates how language, structures, and images may influence audience responses and examines how media representations may structure patterns of misinformation. The audience were poorly informed on asylum and refugee issues. In addition attention is drawn to viewers’ everyday relations and experiences. Some interviewees use specific knowledge to reject news reports. The research provides comprehensive and fruitful insights of cultural differentiation linked to ‘race’/ethnicity, gender, class and geographical location. The thesis concludes by arguing for a media studies schema which connects questions about audience reception with questions about media production and content as well as the construction of broader relations within society enabling researchers to contribute to current debates about power, control and social conditions.
3
Karamanoglu, Sema. "One Historian Two Books: Beatriz Colomina." Master's thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615519/index.pdf.
Повний текст джерелаАнотація:
This thesis aims to explore selected works of Beatriz Colomina, a revisionist architectural historian who has made influential studies on visuality, domesticity, media and gender, and their reflections in the architectural world. Colomina is a distinguished architectural historian since she places a new lens on a period when architecture ceased to be only for the elite and media has gradually penetrated into everyone&rsquos life in order to understand how architecture became accessible to the public through media and how this has affected the perception of modern architecture. This new lens entailed not only the inseparability of media and architecture but also how war and domesticity featured in this relationship. Against this background, this study attempts to investigate the innovative approach of Beatriz Colomina by comparing and contrasting her two prominent books: Privacy and Publicity: Modern Architecture as Mass Media (1994) and Domesticity at War (2007). The former introduces us to the relationship between architecture and media, whereas the latter exemplifies this relationship by focusing on the cold war period as a time where media became an integral part of the domestic environment. This study aims to extract Colomina&rsquo
s contribution to architectural history by first disentangling and analysing and then merging these two books under common themes. In doing so, it seeks to answer the following questions: What is the role of archives in Colomina&rsquo
s methodology in writing these two books? What is the relationship between the document and the historian that emerges from this methodology? What common themes can be extracted from these two books as an analytical framework in order to better understand and study Colomina&rsquo
s approach? What differentiates her as a historian from other historians of modern architecture, specifically from Siegfried Giedion and Kenneth Frampton? What messages does Colomina give her reader through the form as well as the content of her books? What is her contribution to architectural historiography?
4
Mehta, Nishant A. "On sparse representations and new meta-learning paradigms for representation learning." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/52159.
Повний текст джерелаАнотація:
Given the "right" representation, learning is easy. This thesis studies representation learning and meta-learning, with a special focus on sparse representations. Meta-learning is fundamental to machine learning, and it translates to learning to learn itself. The presentation unfolds in two parts. In the first part, we establish learning theoretic results for learning sparse representations. The second part introduces new multi-task and meta-learning paradigms for representation learning.
On the sparse representations front, our main pursuits are generalization error bounds to support a supervised dictionary learning model for Lasso-style sparse coding. Such predictive sparse coding algorithms have been applied with much success in the literature; even more common have been applications of unsupervised sparse coding followed by supervised linear hypothesis learning. We present two generalization error bounds for predictive sparse coding, handling the overcomplete setting (more original dimensions than learned features) and the infinite-dimensional setting. Our analysis led to a fundamental stability result for the Lasso that shows the stability of the solution vector to design matrix perturbations. We also introduce and analyze new multi-task models for (unsupervised) sparse coding and predictive sparse coding, allowing for one dictionary per task but with sharing between the tasks' dictionaries.
The second part introduces new meta-learning paradigms to realize unprecedented types of learning guarantees for meta-learning. Specifically sought are guarantees on a meta-learner's performance on new tasks encountered in an environment of tasks. Nearly all previous work produced bounds on the expected risk, whereas we produce tail bounds on the risk, thereby providing performance guarantees on the risk for a single new task drawn from the environment. The new paradigms include minimax multi-task learning (minimax MTL) and sample variance penalized meta-learning (SVP-ML). Regarding minimax MTL, we provide a high probability learning guarantee on its performance on individual tasks encountered in the future, the first of its kind. We also present two continua of meta-learning formulations, each interpolating between classical multi-task learning and minimax multi-task learning. The idea of SVP-ML is to minimize the task average of the training tasks' empirical risks plus a penalty on their sample variance. Controlling this sample variance can potentially yield a faster rate of decrease for upper bounds on the expected risk of new tasks, while also yielding high probability guarantees on the meta-learner's average performance over a draw of new test tasks. An algorithm is presented for SVP-ML with feature selection representations, as well as a quite natural convex relaxation of the SVP-ML objective.
5
Miglani, Vivek N. "Comparing learned representations of deep neural networks." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123048.
Повний текст джерелаАнотація:
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 63-64).
In recent years, a variety of deep neural network architectures have obtained substantial accuracy improvements in tasks such as image classification, speech recognition, and machine translation, yet little is known about how different neural networks learn. To further understand this, we interpret the function of a deep neural network used for classification as converting inputs to a hidden representation in a high dimensional space and applying a linear classifier in this space. This work focuses on comparing these representations as well as the learned input features for different state-of-the-art convolutional neural network architectures. By focusing on the geometry of this representation, we find that different network architectures trained on the same task have hidden representations which are related by linear transformations. We find that retraining the same network architecture with a different initialization does not necessarily lead to more similar representation geometry for most architectures, but the ResNeXt architecture consistently learns similar features and hidden representation geometry. We also study connections to adversarial examples and observe that networks with more similar hidden representation geometries also exhibit higher rates of adversarial example transferability.
by Vivek N. Miglani.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
6
Bideran, Jessica de. "Infographie, images de synthèse et patrimoine monumental : espace de représentation, espace de médiation." Thesis, Bordeaux 3, 2012. http://www.theses.fr/2012BOR30025/document.
Повний текст джерелаАнотація:
La présente thèse pose la question de l’usage des techniques infographiques et images de synthèse pour représenter les vestiges du passé et plus exactement le patrimoine archéologique et architectural. Cette réflexion est bâtie autour d’une approche multiple. Les dispositifs infographiques appartenant, en effet, à l’ensemble des inventions médiatiques et culturelles nées dans la société d’après-guerre, notre étude se veut profondément transdisciplinaire, empruntant des théories à l’Histoire de l’art, l’Archéologie et les Sciences de l’Information et de la Communication. Le parcours suivi consiste à sortir de la dimension purement technicienne afin d’analyser ces dispositifs comme des espaces culturels de représentation, au sein d’une communauté scientifique (les « spécialistes ») et à destination du grand public (les « néophytes »). La figuration du patrimoine monumental à l’aide des outils infographiques n’est assurément pas apparue spontanément en tant que telle du jour au lendemain, que ce soit dans la sphère publique ou dans la sphère plus restreinte de la communauté des chercheurs. Si ce phénomène est évidemment intimement corrélé à l’évolution du secteur informatique, il serait bien trop simpliste de le considérer comme seulement consécutif de cette révolution technologique. En effet, des transformations des espaces médiatiques et scientifiques se jouent parallèlement. La conservation des monuments historiques et sites archéologiques, leur inscription dans l’espace public ou leur mise en exposition suscitent par conséquent débats et controverses au sein de la sphère des chercheurs et des institutionnels. Ces sujets interpellent plus généralement le concept de patrimoine, autant pour des raisons idéologiques qu’historiques. Il s’agit donc de déceler à travers cette étude les éléments socioculturels qui ont engendré l’émergence et le développement de ces pratiques à la fois graphiques, informatiques et scientifiques. Ainsi défini, le contexte nous donne ensuite accès à l’analyse des usages et des appropriations de ces outils par les différents acteurs de la sphère patrimoniale. Il convient enfin de s’attarder sur la matérialité de ces images et de mettre en évidence les espaces de médiation que créent ces dispositifs. En définitive, il semble que ce que nous donnent à voir ces nouvelles représentations, c'est une hybridation des pratiques de communication et des codes signifiants entre culture « savante » et culture « populaire »This thesis raises the issue of the use of infographic techniques and synthetic imagery to represent vestiges of the past, in particular archaeological and architectural heritage. Our approach is multidisciplinary. Since infographic systems belong to the category of media-related and cultural inventions that have come into existence since World War II, our study aims to be comprehensive, drawing on Art History, Archaeology, Information and Communications Technology. Our intention is to look beyond the purely technical dimension and to analyse these systems as cultural spaces of representation, within the scientific community (“specialists”) and for the general public (“neophytes”). Representation of built heritage using infographic tools has of course not sprung up spontaneously overnight, whether in the public sphere or in the more restricted sphere of the research community. Although this phenomenon is of course closely correlated with the development of the IT sector, it would be simplistic to regard it only as a consequence of this technological revolution. Indeed, changes in the media and scientific fields have gone hand in hand. The conservation of historic monuments and archaeological sites, their listing as being of public interest and management for exhibition purposes, consequently gives rise to debate and controversy in both the scientific-research and institutional spheres. More generally, these matters raise the issue of “heritage”, as much for ideological as for historical reasons. The purpose, then, of this study is to identify the social and cultural factors that have led to the emergence and development of these practices, which involve a combination of graphics, information technology and scientific research. Thus defined, the context invites us to analyse the ways in which these tools have been used and appropriated by different players in the heritage industry. Finally, we need to consider the material aspect of these images and highlight the areas of mediation which these systems create. In conclusion, it would seem that these new modes of representation exemplify a hybridisation of communication practises and codes of meaning resulting from the mixing of “scientific” and “popular” culture
7
Do, Thanh Ha. "Sparse representations over learned dictionary for document analysis." Thesis, Université de Lorraine, 2014. http://www.theses.fr/2014LORR0021/document.
Повний текст джерелаАнотація:
Dans cette thèse, nous nous concentrons sur comment les représentations parcimonieuses peuvent aider à augmenter les performances pour réduire le bruit, extraire des régions de texte, reconnaissance des formes et localiser des symboles dans des documents graphiques. Pour ce faire, tout d'abord, nous donnons une synthèse des représentations parcimonieuses et ses applications en traitement d'images. Ensuite, nous présentons notre motivation pour l'utilisation de dictionnaires d'apprentissage avec des algorithmes efficaces pour les construire. Après avoir décrit l'idée générale des représentations parcimonieuses et du dictionnaire d'apprentissage, nous présentons nos contributions dans le domaine de la reconnaissance de symboles et du traitement des documents en les comparants aux travaux de l'état de l'art. Ces contributions s'emploient à répondre aux questions suivantes: La première question est comment nous pouvons supprimer le bruit des images où il n'existe aucune hypothèse sur le modèle de bruit sous-jacent à ces images ? La deuxième question est comment les représentations parcimonieuses sur le dictionnaire d'apprentissage peuvent être adaptées pour séparer le texte du graphique dans des documents? La troisième question est comment nous pouvons appliquer la représentation parcimonieuse à reconnaissance de symboles? Nous complétons cette thèse en proposant une approche de localisation de symboles dans les documents graphiques qui utilise les représentations parcimonieuses pour coder un vocabulaire visuelIn this thesis, we focus on how sparse representations can help to increase the performance of noise removal, text region extraction, pattern recognition and spotting symbols in graphical documents. To do that, first of all, we give a survey of sparse representations and its applications in image processing. Then, we present the motivation of building learning dictionary and efficient algorithms for constructing a learning dictionary. After describing the general idea of sparse representations and learned dictionary, we bring some contributions in the field of symbol recognition and document processing that achieve better performances compared to the state-of-the-art. These contributions begin by finding the answers to the following questions. The first question is how we can remove the noise of a document when we have no assumptions about the model of noise found in these images? The second question is how sparse representations over learned dictionary can separate the text/graphic parts in the graphical document? The third question is how we can apply the sparse representation for symbol recognition? We complete this thesis by proposing an approach of spotting symbols that use sparse representations for the coding of a visual vocabulary
8
Murray, Joseph F. "Visual recognition, inference and coding using learned sparse overcomplete representations /." Diss., Connect to a 24 p. preview or request complete full text in PDF formate. Access restricted to UC campuses, 2005. http://wwwlib.umi.com/cr/ucsd/fullcit?p3189208.
Повний текст джерела
9
Fratamico, Lauren. "Trade-offs in data representations for learner models in interactive simulations." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/55058.
Повний текст джерелаАнотація:
Interactive simulations can foster student driven, exploratory learning. However, students may not always learn effectively in these unstructured environments. Due to this, it would be advantageous to provide adaptive support to those that are not effectively using the learning environment. To achieve this, it is helpful to build a user-model that can estimate the learner’s trajectories and need for help during interaction. However, this is challenging because it is hard to know a priori which behaviors are conducive to learning. It is particularly challenging in complex Exploratory Learning Environments (like in PhET’s DC Circuit Construction Kit which is used in this work) because of the large variety of ways to interact. To address this problem, we evaluate multiple representations of student interactions with the simulation that capture different amounts of granularity and feature engineering. We then apply the student modeling framework proposed in [1] to mine the student behaviors and classify learners. Our results indicate that the proposed framework is able to extend to a more complex environment in that we are able to successfully classify students and identify behaviors intuitively associated with high and low learning. We also discuss the trade-offs between the differing levels of granularity and feature engineering in the tested interaction representations in terms of their ability to evaluate learning and inform feedback.
[1] Samad Kardan and Cristina Conati. 2011. A Framework for Capturing Distinguishing User Interaction Behaviours in Novel Interfaces. Proceedings of the 4th International Conference on Educational Data Mining, 159–168.Science, Faculty of
Computer Science, Department of
Graduate
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Maas-Olsen, Marcelle Isabel. "Empowering representative councils of learners through policy-making." Thesis, Cape Peninsula University of Technology, 2006. http://hdl.handle.net/20.500.11838/1647.
Повний текст джерелаАнотація:
Thesis (MTech (Public Management))--Cape Peninsula University of Technology, 2006.The right of learners to participate in decision-making as stakeholders in their own education was a significant area of controversy between learners and education authorities prior to 1994. At the end of the apartheid regime in 1994 the foundation was laid for a South Africa based on democratic values, social justice and fundamental human rights as provided for in the Constitution of the Republic of South Africa, 1996 (Act 108 of 1996), hereinafter referred to as the Constitution RSA. To give effect to these constitutional rights and to entrench the democratic values in society, a new system of education and training which required the phasing-in of new education legislation had to be created. The National Education Policy Act, 1996 (Act 27 of 1996) [NEPAl was the first comprehensive new act promulgated by the government after 1994. This act mainly provides for the promulgation of education policy by the Minister of Education. The South African Schools Act, 1996 (Act 84 of 1996) [SASAj, as amended, provides a national system of school education that advances democracy, the development of all leamers and the protection of rights, as well as promoting acceptance of responsibility by learners, parents and educators for the organisation of the school, its governance and its funding. The SASA has entrenched the rights of learners to participate as stakeholders in education by affording them representation in school governing bodies which have the status of being the only legitimate bodies representing parents and learners in public schools.
Книги з теми "Learned representation"
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1949-, Brachman Ronald J., Levesque Hector J. 1951-, Reiter Raymond, and Canadian Society for Computational Studies of Intelligence., eds. Proceedings of the First International Conference on Principles of Knowledge Representation and Reasoning. San Mateo, Calif: M. Kaufmann, 1989.
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Sangiuliano, Maria, and Agostino Cortesi. Institutional Change for Gender Equality in Research Lesson Learned from the Field. Venice: Fondazione Università Ca’ Foscari, 2019. http://dx.doi.org/10.30687/978-88-6969-334-2.
Повний текст джерелаАнотація:
Gender balance in research organizations is considered as a key step for ensuring research excellence and quality and inclusive-sustainable innovation. Still, in spite of an increasing number of HE and research institutions committed to make science more equal and some positive trends in figures on Gender equality in STEM research, it still appears to be difficult to prioritize gender equality. This is particularly true for disciplines such as ICT/IST where female representation at all levels is among the lowest ones among STEM topics and where a gender sensitive approach to ICT design and programming is far from being understood in its implications among computer and information systems scientist. H2020 (PGERI and SWAFS programmes in particular), promoted the concept of institutional change for gender equality, insisting on the need for merging change management and gender policies. The volume is focusing on a presentation and reflexive review of results and tools from the H2020 EQUAL-IST project to discuss opportunities to innovate and transform HR management and Institutional communication, research design, teaching & students services, via gender equality, and how such innovations could be multiplied and sustained with a focus on ICT and IST research organizations. The volume is complemented by contributions from other projects on institutional change in research.
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Luo, Yingmei. A New Representation of Chinese Learners. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2152-9.
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Tytler, Russell, Vaughan Prain, Peter Hubber, and Bruce Waldrip, eds. Constructing Representations to Learn in Science. Rotterdam: SensePublishers, 2013. http://dx.doi.org/10.1007/978-94-6209-203-7.
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Paprika, Zita Zoltay. Representation and support of decision making: A case where the analysts could learn as much as the stakeholdersdid. Budapest: Karl Marx University of Economics, 1989.
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Programme/Cambodia, United Nations Development. Strengthening democracy and electoral processes in Cambodia: Lessons learnt and best practices in promoting women participation and representation in Cambodia 2010. [Phnom Penh]: UNDP Cambodia, 2010.
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Rey, Georges. Representation of Language. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198855637.001.0001.
Повний текст джерелаАнотація:
This book is a defense, against mostly philosophical objections, of a Chomskyan postulation of an internal, innate computational system for human language that is typically manifested in native speaker’s intuitive responses to samples of speech. But it is also a critical examination of some of the glosses on the theory: the assimilation of it to traditional Rationalism; a supposed conflict between being innate and learned; an unclear ontology which requires what I call a “representational pretense” (whereby linguists merely pretend for the sake of exposition that, e.g., tokens of words are uttered); and, most crucially to my concerns, Chomsky’s specific eliminativism about the role of intentionality not only in his own theories, but in any serious science at all. This last is a fundamentally important issue for linguistics, psychology, and philosophy that I hope an examination of a theory as rich and promising as a Chomskyan linguistics will help illuminate. I will also touch on some peripheral issues that Chomsky seems to me to mistakenly associate with his theory: an anti-realism about ordinary thought and talk, and a peculiar dismissal of the mind/body problem(s), toward the solution of some of which I think his theory actually makes a promising contribution.
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Douglas, Greenberg, and American Council of Learned Societies., eds. Constitutionalism and democracy: Transitions in the contemporary world : the American Council of Learned Societies comparative constitutionalism papers. New York: Oxford University Press, 1993.
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Levesque, Hector J., International Conference on Principles of Knowledge Representation and, Raymond Reiter, Ronald J. Brachman, and Canadian Society for Computational Studies of Intelligence. KR Proceedings 1989 (Morgan-Kaufmann Series in Representation and Reasoning). Morgan Kaufmann, 1989.
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Gillespie, Caitlin C. We Learned These Things from the Romans. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190609078.003.0005.
Повний текст джерелаАнотація:
Chapter 4 analyzes Dio’s representation of Boudica as an emblem of barbarian strength and fortitude who criticizes the misplaced values of the Romans. Boudica’s fearsome visage opens the conversation. Her appearance has parallels in Diodorus Siculus’s description of the Gauls, and material evidence of East Anglia provides support for her wearing a gold torc (a type of metal band worn around the neck). Images of the personified Britannia and other non-Romans suggest the models Dio is working against in his depiction of Boudica. Boudica’s speech in Dio responds to other female speeches, from Hersilia, to Veturia, to the empress Livia. In her speech, Boudica comments on the failures of Nero’s regime and the lack of imperial models of traditional Roman morality.
Частини книг з теми "Learned representation"
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Cowart, Monica R. "Representation Revisited: Lessons Learned from Artificial Life." In Proceedings of the Twentieth Annual Conference of the Cognitive Science Society, 1212. New York: Routledge, 2022. http://dx.doi.org/10.4324/9781315782416-232.
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Wang, Juan, Guanghui Li, Fei Du, Meng Wang, Yong Hu, Meng Yu, Aiyun Zhan, and Yuejin Zhang. "Image Denoising Based on Sparse Representation over Learned Dictionaries." In Cyberspace Safety and Security, 479–86. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37352-8_41.
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Fu, Zhenyong, Hongtao Lu, Nan Deng, and Nengbin Cai. "Large Scale Visual Classification via Learned Dictionaries and Sparse Representation." In Artificial Intelligence and Computational Intelligence, 321–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16530-6_38.
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Gudovskiy, Denis, Alec Hodgkinson, and Luca Rigazio. "DNN Feature Map Compression Using Learned Representation over GF(2)." In Lecture Notes in Computer Science, 502–16. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11018-5_41.
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Manalo, Emmanuel, and Mari Fukuda. "Diagrams in Essays: Exploring the Kinds of Diagrams Students Generate and How Well They Work." In Diagrammatic Representation and Inference, 553–61. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86062-2_56.
Повний текст джерелаАнотація:
AbstractUsing appropriate diagrams is generally considered efficacious in communication. However, although diagrams are extensively used in printed and digital media, people in general rarely construct diagrams to use in common everyday communication. Furthermore, instruction on diagram use for communicative purposes is uncommon in formal education and, when students are required to communicate what they have learned, the usual expectation is they will use words – not diagrams. Requiring diagram inclusion in essays, for example, would be almost unheard of. Consequently, current understanding about student capabilities in this area is very limited. The aim of this study therefore was to contribute to addressing this gap: it comprised a qualitative exploration of 12 undergraduate students’ diagram use in two essays (in which they were asked to include at least one diagram). Analysis focused on identifying the kinds of diagrams produced, and the effectiveness with which those diagrams were used. Useful functions that the diagrams served included clarification, summarization, integration of points, and provision of additional information and/or perspectives in visual form. However, there were also redundancies, as well as unclear, schematically erroneous, and overly complicated representations in some of the diagrams that the students constructed. These findings are discussed in terms of needs, opportunities, and challenges in instructional provision.
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Bentley, Peter J., Soo Ling Lim, Adam Gaier, and Linh Tran. "Evolving Through the Looking Glass: Learning Improved Search Spaces with Variational Autoencoders." In Lecture Notes in Computer Science, 371–84. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14714-2_26.
Повний текст джерелаАнотація:
AbstractNature has spent billions of years perfecting our genetic representations, making them evolvable and expressive. Generative machine learning offers a shortcut: learn an evolvable latent space with implicit biases towards better solutions. We present SOLVE: Search space Optimization with Latent Variable Evolution, which creates a dataset of solutions that satisfy extra problem criteria or heuristics, generates a new latent search space, and uses a genetic algorithm to search within this new space to find solutions that meet the overall objective. We investigate SOLVE on five sets of criteria designed to detrimentally affect the search space and explain how this approach can be easily extended as the problems become more complex. We show that, compared to an identical GA using a standard representation, SOLVE with its learned latent representation can meet extra criteria and find solutions with distance to optimal up to two orders of magnitude closer. We demonstrate that SOLVE achieves its results by creating better search spaces that focus on desirable regions, reduce discontinuities, and enable improved search by the genetic algorithm.
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Zhou, Bolei. "Interpreting Generative Adversarial Networks for Interactive Image Generation." In xxAI - Beyond Explainable AI, 167–75. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_9.
Повний текст джерелаАнотація:
AbstractSignificant progress has been made by the advances in Generative Adversarial Networks (GANs) for image generation. However, there lacks enough understanding of how a realistic image is generated by the deep representations of GANs from a random vector. This chapter gives a summary of recent works on interpreting deep generative models. The methods are categorized into the supervised, the unsupervised, and the embedding-guided approaches. We will see how the human-understandable concepts that emerge in the learned representation can be identified and used for interactive image generation and editing.
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Liu, Xiaofeng, Tong Che, Yiqun Lu, Chao Yang, Site Li, and Jane You. "AUTO3D: Novel View Synthesis Through Unsupervisely Learned Variational Viewpoint and Global 3D Representation." In Computer Vision – ECCV 2020, 52–71. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58545-7_4.
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Konoplich, Georgy, Evgeniy Putin, Andrey Filchenkov, and Roman Rybka. "Named Entity Recognition in Russian with Word Representation Learned by a Bidirectional Language Model." In Communications in Computer and Information Science, 48–58. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01204-5_5.
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Malaisé, Véronique, Anke Otten, and Pascal Coupet. "OmniScience and Extensions – Lessons Learned from Designing a Multi-domain, Multi-use Case Knowledge Representation System." In Lecture Notes in Computer Science, 228–42. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03667-6_15.
Повний текст джерелаТези доповідей конференцій з теми "Learned representation"
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Xie, Ruobing, Zhiyuan Liu, Huanbo Luan, and Maosong Sun. "Image-embodied Knowledge Representation Learning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/438.
Повний текст джерелаАнотація:
Entity images could provide significant visual information for knowledge representation learning. Most conventional methods learn knowledge representations merely from structured triples, ignoring rich visual information extracted from entity images. In this paper, we propose a novel Image-embodied Knowledge Representation Learning model (IKRL), where knowledge representations are learned with both triple facts and images. More specifically, we first construct representations for all images of an entity with a neural image encoder. These image representations are then integrated into an aggregated image-based representation via an attention-based method. We evaluate our IKRL models on knowledge graph completion and triple classification. Experimental results demonstrate that our models outperform all baselines on both tasks, which indicates the significance of visual information for knowledge representations and the capability of our models in learning knowledge representations with images.
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Gao, Li, Hong Yang, Chuan Zhou, Jia Wu, Shirui Pan, and Yue Hu. "Active Discriminative Network Representation Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/296.
Повний текст джерелаАнотація:
Most of current network representation models are learned in unsupervised fashions, which usually lack the capability of discrimination when applied to network analysis tasks, such as node classification. It is worth noting that label information is valuable for learning the discriminative network representations. However, labels of all training nodes are always difficult or expensive to obtain and manually labeling all nodes for training is inapplicable. Different sets of labeled nodes for model learning lead to different network representation results. In this paper, we propose a novel method, termed as ANRMAB, to learn the active discriminative network representations with a multi-armed bandit mechanism in active learning setting. Specifically, based on the networking data and the learned network representations, we design three active learning query strategies. By deriving an effective reward scheme that is closely related to the estimated performance measure of interest, ANRMAB uses a multi-armed bandit mechanism for adaptive decision making to select the most informative nodes for labeling. The updated labeled nodes are then used for further discriminative network representation learning. Experiments are conducted on three public data sets to verify the effectiveness of ANRMAB.
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B. C., Haris, and Rohit Sinha. "Sparse representation over learned and discriminatively learned dictionaries for speaker verification." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6288989.
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Wang, Hu, Guansong Pang, Chunhua Shen, and Congbo Ma. "Unsupervised Representation Learning by Predicting Random Distances." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/408.
Повний текст джерелаАнотація:
Deep neural networks have gained great success in a broad range of tasks due to its remarkable capability to learn semantically rich features from high-dimensional data. However, they often require large-scale labelled data to successfully learn such features, which significantly hinders their adaption in unsupervised learning tasks, such as anomaly detection and clustering, and limits their applications to critical domains where obtaining massive labelled data is prohibitively expensive. To enable unsupervised learning on those domains, in this work we propose to learn features without using any labelled data by training neural networks to predict data distances in a randomly projected space. Random mapping is a theoretically proven approach to obtain approximately preserved distances. To well predict these distances, the representation learner is optimised to learn genuine class structures that are implicitly embedded in the randomly projected space. Empirical results on 19 real-world datasets show that our learned representations substantially outperform a few state-of-the-art methods for both anomaly detection and clustering tasks. Code is available at: \url{https://git.io/RDP}
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Chen, Zhenpeng, Sheng Shen, Ziniu Hu, Xuan Lu, Qiaozhu Mei, and Xuanzhe Liu. "Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification (Extended Abstract)." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/649.
Повний текст джерелаАнотація:
Sentiment classification typically relies on a large amount of labeled data. In practice, the availability of labels is highly imbalanced among different languages. To tackle this problem, cross-lingual sentiment classification approaches aim to transfer knowledge learned from one language that has abundant labeled examples (i.e., the source language, usually English) to another language with fewer labels (i.e., the target language). The source and the target languages are usually bridged through off-the-shelf machine translation tools. Through such a channel, cross-language sentiment patterns can be successfully learned from English and transferred into the target languages. This approach, however, often fails to capture sentiment knowledge specific to the target language. In this paper, we employ emojis, which are widely available in many languages, as a new channel to learn both the cross-language and the language-specific sentiment patterns. We propose a novel representation learning method that uses emoji prediction as an instrument to learn respective sentiment-aware representations for each language. The learned representations are then integrated to facilitate cross-lingual sentiment classification.
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Wu, Qian, Rong Zhang, and Dawei Xu. "Hyperspectral image representation using learned multiscale dictionaries." In 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2014. http://dx.doi.org/10.1109/whispers.2014.8077501.
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Van Noord, Nanne, and Eric Postma. "A Learned Representation of Artist-Specific Colourisation." In 2017 IEEE International Conference on Computer Vision Workshop (ICCVW). IEEE, 2017. http://dx.doi.org/10.1109/iccvw.2017.343.
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Lopes, Raphael Gontijo, David Ha, Douglas Eck, and Jonathon Shlens. "A Learned Representation for Scalable Vector Graphics." In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00802.
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Sang, Tong, Hongyao Tang, Yi Ma, Jianye Hao, Yan Zheng, Zhaopeng Meng, Boyan Li, and Zhen Wang. "PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/474.
Повний текст джерелаАнотація:
Deep Reinforcement Learning (DRL) has been a promising solution to many complex decision-making problems. Nevertheless, the notorious weakness in generalization among environments prevent widespread application of DRL agents in real-world scenarios. Although advances have been made recently, most prior works assume sufficient online interaction on training environments, which can be costly in practical cases. To this end, we focus on an offline-training-online-adaptation setting, in which the agent first learns from offline experiences collected in environments with different dynamics and then performs online policy adaptation in environments with new dynamics. In this paper, we propose Policy Adaptation with Decoupled Representations (PAnDR) for fast policy adaptation. In offline training phase, the environment representation and policy representation are learned through contrastive learning and policy recovery, respectively. The representations are further refined by mutual information optimization to make them more decoupled and complete. With learned representations, a Policy-Dynamics Value Function (PDVF) network is trained to approximate the values for different combinations of policies and environments from offline experiences. In online adaptation phase, with the environment context inferred from few experiences collected in new environments, the policy is optimized by gradient ascent with respect to the PDVF. Our experiments show that PAnDR outperforms existing algorithms in several representative policy adaptation problems.
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Wang, Pengyang, Yanjie Fu, Yuanchun Zhou, Kunpeng Liu, Xiaolin Li, and Kien Hua. "Exploiting Mutual Information for Substructure-aware Graph Representation Learning." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/472.
Повний текст джерелаАнотація:
In this paper, we design and evaluate a new substructure-aware Graph Representation Learning (GRL) approach. GRL aims to map graph structure information into low-dimensional representations. While extensive efforts have been made for modeling global and/or local structure information, GRL can be improved by substructure information. Some recent studies exploit adversarial learning to incorporate substructure awareness, but hindered by unstable convergence. This study will address the major research question: is there a better way to integrate substructure awareness into GRL? As subsets of the graph structure, interested substructures (i.e., subgraph) are unique and representative for differentiating graphs, leading to the high correlation between the representation of the graph-level structure and substructures. Since mutual information (MI) is to evaluate the mutual dependence between two variables, we develop a MI inducted substructure-aware GRL method. We decompose the GRL pipeline into two stages: (1) node-level, where we introduce to maximize MI between the original and learned representation by the intuition that the original and learned representation should be highly correlated; (2) graph-level, where we preserve substructures by maximizing MI between the graph-level structure and substructure representation. Finally, we present extensive experimental results to demonstrate the improved performances of our method with real-world data.
Звіти організацій з теми "Learned representation"
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Lucas, Brian. Lessons Learned about Political Inclusion of Refugees. Institute of Development Studies, May 2022. http://dx.doi.org/10.19088/k4d.2022.114.
Повний текст джерелаАнотація:
Most refugees and other migrants have limited opportunities to participate in politics to inform and influence the policies that affect them daily; they have limited voting rights and generally lack effective alternative forms of representation such as consultative bodies (Solano & Huddleston, 2020a, p. 33). Political participation is ‘absent (or almost absent) from integration strategies’ in Eastern European countries, while refugees and other migrants in Western Europe do enjoy significant local voting rights, stronger consultative bodies, more funding for immigrant organisations and greater support from mainstream organisations (Solano & Huddleston, 2020a, p. 33).This rapid review seeks to find out what lessons have been learned about political inclusion of refugees, particularly in European countries.In general, there appears to be limited evidence about the effectiveness of attempts to support the political participation of migrants/refugees. ‘The engagement of refugees and asylum-seekers in the political activities of their host countries is highly understudied’ (Jacobi, 2021, p. 3) and ‘the effects that integration policies have on immigrants’ representation remains an under-explored field’ (Petrarca, 2015, p. 9). The evidence that is available often comes from sources that cover the entire population or ethnic minorities without specifically targeting refugees or migrants, are biased towards samples of immigrants who are long-established in the host country and may not be representative of immigrant populations, or focus only on voting behaviour and neglect other forms of political participation (Bilodeau, 2016, pp. 30–31). Statistical data on refugees and integration policy areas and indicators is often weak or absent (Hopkins, 2013, pp. 9, 28–32, 60). Data may not distinguish clearly among refugees and other types of migrants by immigration status, origin country, or length of stay in the host country; may not allow correlating data collected during different time periods with policies in place during those periods and preceding periods; and may fail to collect a range of relevant migrant-specific social and demographic characteristics (Bilgili et al., 2015, pp. 22–23; Hopkins, 2013, p. 28).
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Raulet, Gérard. What Happens is Unimaginable! About the „Yellow Vests“. Association Inter-University Centre Dubrovnik, February 2021. http://dx.doi.org/10.53099/ntkd4303.
Повний текст джерелаАнотація:
The French ‘yellow vests’ movement is anything but an episodic protest movement. It questions both the liberal and the republican conception of political representation. The reason for this radicalism is that it shakes the foundations of a neo-capitalist order, for which shortterm financial sales have become more important than the long-term maintenance of the system itself. From the financial crisis of 2008, neoliberalism only seems to have learned that,despite everything, the model on which it is based holds up. This creates a profound crisis of legitimacy that reveals a break in political culture that no policy of consensus or even recognition can remedy. This essay examines the theoretical approaches that can take this phenomenon into account.
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Armas, Elvira, Magaly Lavadenz, and Laurie Olsen. Falling Short on The Promise to English Learners: A Report on Year One LCAPs. Center for Equity for English Learners, 2015. http://dx.doi.org/10.15365/ceel.lcap2015.2.
Повний текст джерелаАнотація:
California’s Local Control Funding Formula was signed into law in California in 2013 and allowed districts the flexibility to meet their student needs in locally appropriate manners. One year after its implementation, a panel of 26 reviewers, including educators, English Learner (EL) advocates, and legal services staff reviewed the Local Control and Accountability Plans (LCAPs) to understand how districts employ this flexibility to address the needs of ELs. The report uses the English Learner Research-Aligned LCAP Rubrics with 10 focus areas, and reviews sample LCAPs from 29 districts, including districts with the highest numbers/percentages of English Learners in the state, districts representative of California’s geographic Regions, and districts providing quality EL services. The review centers around four questions of the extent to which first-year LCAPs: (1) specify goals and identify outcomes for ELs, (2) identify action steps and allocate funds for increased or improved services for all types of ELs, (3) reflect research-based practices for achieving language proficiency and academic achievement for English Learners in their actions, programs and services, and (4) are designed and implemented with EL parent input as reflected in stakeholder engagement. The results indicate that overall, the LCAP is inadequate as part of the state’s public accountability system in ensuring equity and access for ELs. Six key findings were: (1) difficulty in discerning funding allocations related to EL services and programs; (2) inability to identify districts’ plans for increased services for ELs; (3) lack of explicitly specified services and programs aligned to EL needs; (4) weak approach or missing English Language Development (ELD) or implementation of ELD standards in most LCAPs; (5) weak/inconsistent representation of EL parent engagement; and (6) lack of EL student outcome measures. The authors also present detailed findings for each focus topic and offer district and state level recommendations.
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Estrada-Miller, Jeimee, Leni Wolf, Elvira Armas, and Magaly Lavadenz. Uplifting the Perspectives and Preferences of the Families of English Learners in Los Angeles Unified School District and Charter Schools: Findings from a Representative Poll. Loyola Marymount University, 2022. http://dx.doi.org/10.15365/ceel.policy.11.
Повний текст джерелаАнотація:
This research and policy brief uplifts findings from a 2021 poll of 129 LAUSD and affiliate charter school English Learner families. The poll covers a broad range of topics including families’ pandemic experiences in and outside of school, communication with schools, levels of engagement and representation in school-based decisions, and expectations of schools for the future. Findings indicate that: (1) a majority of EL families are engaged and report that they attend school activities; (2) EL families report feeling heard at their school sites and would like more personalized communication like home visits and calls; (3) EL families want more information about their child’s academic and English language development; and (4) EL Families want schools to rethink how they educate students, including more one-on-one academic support and wrap-around services. Based on these findings, the authors make both short- and long-term recommendations for policy and practice. This brief is intended to be used as a supplement to the full report—a joint effort by Great Public Schools Now, Loyola Marymount University’s Center for Equity for English Learners, and Families in Schools which captures perspectives of 500 English learner and non-English learner families.
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Tessum, Christopher, Qi Tang, Lei Zhao, and Nicole Riemer. Learned implicit representations of aerosol chemistry and physics for enhancing the predictability of water cycle extreme events. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769735.
Повний текст джерела
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Bolton, Laura. Criminal Activity and Deforestation in Latin America. Institute of Development Studies (IDS), December 2020. http://dx.doi.org/10.19088/k4d.2021.003.
Повний текст джерелаАнотація:
This review examines evidence on criminal deforestation activity in Latin America (particularly, but not exclusively the Amazon) and draws from the literature on the lessons learned in combatting criminal deforestation activity. This review focuses on Brazil as representative of the overwhelming majority of literature on criminal activity in relation to deforestation in the Amazon. The literature notes that Illegal deforestation occurs largely through criminal networks as they have the capacity for coordination, processing, selling, and the deployment of armed men to protect operations. Bribery, corruption, and fraud are deeply ingrained in deforestation. Networks may bribe geoprocessing experts, police, and public officials. Members of the criminal groups may become council members, mayors, and state representatives. Land titles are fabricated and trading documentation fraudulent. The literature also notes some interventions to combat this criminal deforestation activity: monitoring and law enforcement; national systems for registry and monitoring; legal enforcement for compliance of environmental law; International agreements and action; and Involving indigenous communities in combatting deforestation.
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Borchmann, Daniel. On Confident GCIs of Finite Interpretations. Technische Universität Dresden, 2012. http://dx.doi.org/10.25368/2022.190.
Повний текст джерелаАнотація:
In the work of Baader and Distel, a method has been proposed to axiomatize all general concept inclusions (GCIs) expressible in the description logic EL⊥ and valid in a given interpretation I. This provides us with an effective method to learn EL⊥-ontologies from interpretations, which itself can be seen as a different representation of linked data. In another report, we have extended this approach to handle errors in the data. This has been done by not only considering valid GCIs but also those whose confidence is above a certain threshold 𝑐. In the present work, we shall extend the results by describing another way to compute bases of confident GCIs. We furthermore provide experimental evidence that this approach can be useful for practical applications. We finally show that the technique of unravelling can also be used to effectively turn confident EL⊥gfp-bases into EL⊥-bases.
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Iatsyshyn, Anna V., Valeriia O. Kovach, Yevhen O. Romanenko, Iryna I. Deinega, Andrii V. Iatsyshyn, Oleksandr O. Popov, Yulii G. Kutsan, Volodymyr O. Artemchuk, Oleksandr Yu Burov, and Svitlana H. Lytvynova. Application of augmented reality technologies for preparation of specialists of new technological era. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3749.
Повний текст джерелаАнотація:
Augmented reality is one of the most modern information visualization technologies. Number of scientific studies on different aspects of augmented reality technology development and application is analyzed in the research. Practical examples of augmented reality technologies for various industries are described. Very often augmented reality technologies are used for: social interaction (communication, entertainment and games); education; tourism; areas of purchase/sale and presentation. There are various scientific and mass events in Ukraine, as well as specialized training to promote augmented reality technologies. There are following results of the research: main benefits that educational institutions would receive from introduction of augmented reality technology are highlighted; it is determined that application of augmented reality technologies in education would contribute to these technologies development and therefore need increase for specialists in the augmented reality; growth of students' professional level due to application of augmented reality technologies is proved; adaptation features of augmented reality technologies in learning disciplines for students of different educational institutions are outlined; it is advisable to apply integrated approach in the process of preparing future professionals of new technological era; application of augmented reality technologies increases motivation to learn, increases level of information assimilation due to the variety and interactivity of its visual representation. Main difficulties of application of augmented reality technologies are financial, professional and methodical. Following factors are necessary for introduction of augmented reality technologies: state support for such projects and state procurement for development of augmented reality technologies; conduction of scientific research and experimental confirmation of effectiveness and pedagogical expediency of augmented reality technologies application for training of specialists of different specialties; systematic conduction of number of national and international events on dissemination and application of augmented reality technology. It is confirmed that application of augmented reality technologies is appropriate for training of future specialists of new technological era.
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Learning About Women and Urban Services in Latin America and the Caribbean. Population Council, 1986. http://dx.doi.org/10.31899/pgy1986.1000.
Повний текст джерелаАнотація:
In 1978 when the Population Council formulated a program to learn more about low-income urban women’s access to services, the dearth of information was striking, particularly in contrast to the emerging body of information delineating access to credit, extension, membership in rural institutions, and representation in local governments. Access to services was much less well-defined owing to the diverse cultures that meet in the urban environment, the mobility of city life, and the fluidity of households. Urban development planners, researchers, and those involved in community action projects in a number of South American cities were approached to find out what they knew, and there was much interest on the part of urban planners in learning how their programs affected men and women differentially. The interest of these diverse groups called for a long-term approach. Three working groups on Women, Low-Income Households, and Urban Services evolved in Kingston, Jamaica; Lima, Peru; and Mexico City, Mexico. Much detail is provided in this volume on how these groups function and arrive at their priorities. Rather than confining this report to a lengthy internal document, this work was brought to the attention of a broader audience through summary articles.
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An assessment of community-based family planning programs in Kenya. Population Council, 1997. http://dx.doi.org/10.31899/rh1997.1006.
Повний текст джерелаАнотація:
Kenya has a long history of using community-based distribution (CBD) as an integral part of its family planning (FP) program. The purpose of this study was to assess the role of CBD programs in terms of providing information and services, to learn more about the determinants of program effectiveness, and to attempt to compare the programs’ cost-effectiveness. Fieldwork was undertaken in mid-1995 when data were collected from seven of the major CBD programs in Kenya. Four rural and three urban programs were included, as were programs that remunerated their agents and those that did not, and programs that had full-time agents and those that had part-time agents. Data on the programs themselves were collected from records and from interviews with managers and staff, as well as from a sample of CBD agents. Data were also collected from representative samples of the population living in the agents’ catchment areas. Key findings and programmatic recommendations are provided in this report.