Bibliografías temáticas / Learning with Limited Data

Literatura académica sobre el tema "Learning with Limited Data"

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Publicado: 18 de mayo de 2024

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Artículos de revistas sobre el tema "Learning with Limited Data"

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Oh, Se Eun, Nate Mathews, Mohammad Saidur Rahman, Matthew Wright y Nicholas Hopper. "GANDaLF: GAN for Data-Limited Fingerprinting". Proceedings on Privacy Enhancing Technologies 2021, n.º 2 (29 de enero de 2021): 305–22. http://dx.doi.org/10.2478/popets-2021-0029.

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Abstract We introduce Generative Adversarial Networks for Data-Limited Fingerprinting (GANDaLF), a new deep-learning-based technique to perform Website Fingerprinting (WF) on Tor traffic. In contrast to most earlier work on deep-learning for WF, GANDaLF is intended to work with few training samples, and achieves this goal through the use of a Generative Adversarial Network to generate a large set of “fake” data that helps to train a deep neural network in distinguishing between classes of actual training data. We evaluate GANDaLF in low-data scenarios including as few as 10 training instances per site, and in multiple settings, including fingerprinting of website index pages and fingerprinting of non-index pages within a site. GANDaLF achieves closed-world accuracy of 87% with just 20 instances per site (and 100 sites) in standard WF settings. In particular, GANDaLF can outperform Var-CNN and Triplet Fingerprinting (TF) across all settings in subpage fingerprinting. For example, GANDaLF outperforms TF by a 29% margin and Var-CNN by 38% for training sets using 20 instances per site.
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Triantafillou, Sofia y Greg Cooper. "Learning Adjustment Sets from Observational and Limited Experimental Data". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 11 (18 de mayo de 2021): 9940–48. http://dx.doi.org/10.1609/aaai.v35i11.17194.

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Estimating causal effects from observational data is not always possible due to confounding. Identifying a set of appropriate covariates (adjustment set) and adjusting for their influence can remove confounding bias; however, such a set is often not identifiable from observational data alone. Experimental data allow unbiased causal effect estimation, but are typically limited in sample size and can therefore yield estimates of high variance. Moreover, experiments are often performed on a different (specialized) population than the population of interest. In this work, we introduce a method that combines large observational and limited experimental data to identify adjustment sets and improve the estimation of causal effects for a target population. The method scores an adjustment set by calculating the marginal likelihood for the experimental data given an observationally-derived causal effect estimate, using a putative adjustment set. The method can make inferences that are not possible using constraint-based methods. We show that the method can improve causal effect estimation, and can make additional inferences when compared to state-of-the-art methods.
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Zhao, Yao, Dong Joo Rhee, Carlos Cardenas, Laurence E. Court y Jinzhong Yang. "Training deep‐learning segmentation models from severely limited data". Medical Physics 48, n.º 4 (19 de febrero de 2021): 1697–706. http://dx.doi.org/10.1002/mp.14728.

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Kim, Minjeong, Yujung Gil, Yuyeon Kim y Jihie Kim. "Deep-Learning-Based Scalp Image Analysis Using Limited Data". Electronics 12, n.º 6 (14 de marzo de 2023): 1380. http://dx.doi.org/10.3390/electronics12061380.

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The World Health Organization and Korea National Health Insurance assert that the number of alopecia patients is increasing every year, and approximately 70 percent of adults suffer from scalp problems. Although alopecia is a genetic problem, it is difficult to diagnose at an early stage. Although deep-learning-based approaches have been effective for medical image analyses, it is challenging to generate deep learning models for alopecia detection and analysis because creating an alopecia image dataset is challenging. In this paper, we present an approach for generating a model specialized for alopecia analysis that achieves high accuracy by applying data preprocessing, data augmentation, and an ensemble of deep learning models that have been effective for medical image analyses. We use an alopecia image dataset containing 526 good, 13,156 mild, 3742 moderate, and 825 severe alopecia images. The dataset was further augmented by applying normalization, geometry-based augmentation (rotate, vertical flip, horizontal flip, crop, and affine transformation), and PCA augmentation. We compare the performance of a single deep learning model using ResNet, ResNeXt, DenseNet, XceptionNet, and ensembles of these models. The best result was achieved when DenseNet, XceptionNet, and ResNet were combined to achieve an accuracy of 95.75 and an F1 score of 87.05.
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Chen, Jiaao, Derek Tam, Colin Raffel, Mohit Bansal y Diyi Yang. "An Empirical Survey of Data Augmentation for Limited Data Learning in NLP". Transactions of the Association for Computational Linguistics 11 (2023): 191–211. http://dx.doi.org/10.1162/tacl_a_00542.

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Abstract NLP has achieved great progress in the past decade through the use of neural models and large labeled datasets. The dependence on abundant data prevents NLP models from being applied to low-resource settings or novel tasks where significant time, money, or expertise is required to label massive amounts of textual data. Recently, data augmentation methods have been explored as a means of improving data efficiency in NLP. To date, there has been no systematic empirical overview of data augmentation for NLP in the limited labeled data setting, making it difficult to understand which methods work in which settings. In this paper, we provide an empirical survey of recent progress on data augmentation for NLP in the limited labeled data setting, summarizing the landscape of methods (including token-level augmentations, sentence-level augmentations, adversarial augmentations, and hidden-space augmentations) and carrying out experiments on 11 datasets covering topics/news classification, inference tasks, paraphrasing tasks, and single-sentence tasks. Based on the results, we draw several conclusions to help practitioners choose appropriate augmentations in different settings and discuss the current challenges and future directions for limited data learning in NLP.
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Han, Te, Chao Liu, Rui Wu y Dongxiang Jiang. "Deep transfer learning with limited data for machinery fault diagnosis". Applied Soft Computing 103 (mayo de 2021): 107150. http://dx.doi.org/10.1016/j.asoc.2021.107150.

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Ji, Xuefei, Jue Wang, Ye Li, Qiang Sun, Shi Jin y Tony Q. S. Quek. "Data-Limited Modulation Classification With a CVAE-Enhanced Learning Model". IEEE Communications Letters 24, n.º 10 (octubre de 2020): 2191–95. http://dx.doi.org/10.1109/lcomm.2020.3004877.

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Forestier, Germain y Cédric Wemmert. "Semi-supervised learning using multiple clusterings with limited labeled data". Information Sciences 361-362 (septiembre de 2016): 48–65. http://dx.doi.org/10.1016/j.ins.2016.04.040.

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Wen, Jiahui y Zhiying Wang. "Learning general model for activity recognition with limited labelled data". Expert Systems with Applications 74 (mayo de 2017): 19–28. http://dx.doi.org/10.1016/j.eswa.2017.01.002.

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Zhang, Ansi, Shaobo Li, Yuxin Cui, Wanli Yang, Rongzhi Dong y Jianjun Hu. "Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning". IEEE Access 7 (2019): 110895–904. http://dx.doi.org/10.1109/access.2019.2934233.

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Tesis sobre el tema "Learning with Limited Data"

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Chen, Si. "Active Learning Under Limited Interaction with Data Labeler". Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104894.

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Active learning (AL) aims at reducing labeling effort by identifying the most valuable unlabeled data points from a large pool. Traditional AL frameworks have two limitations: First, they perform data selection in a multi-round manner, which is time-consuming and impractical. Second, they usually assume that there are a small amount of labeled data points available in the same domain as the data in the unlabeled pool. In this thesis, we initiate the study of one-round active learning to solve the first issue. We propose DULO, a general framework for one-round setting based on the notion of data utility functions, which map a set of data points to some performance measure of the model trained on the set. We formulate the one-round active learning problem as data utility function maximization. We then propose D²ULO on the basis of DULO as a solution that solves both issues. Specifically, D²ULO leverages the idea of domain adaptation (DA) to train a data utility model on source labeled data. The trained utility model can then be used to select high-utility data in the target domain and at the same time, provide an estimate for the utility of the selected data. Our experiments show that the proposed frameworks achieves better performance compared with state-of-the-art baselines in the same setting. Particularly, D²ULO is applicable to the scenario where the source and target labels have mismatches, which is not supported by the existing works.
M.S.
Machine Learning (ML) has achieved huge success in recent years. Machine Learning technologies such as recommendation system, speech recognition and image recognition play an important role on human daily life. This success mainly build upon the use of large amount of labeled data: Compared with traditional programming, a ML algorithm does not rely on explicit instructions from human; instead, it takes the data along with the label as input, and aims to learn a function that can correctly map data to the label space by itself. However, data labeling requires human effort and could be time-consuming and expensive especially for datasets that contain domain-specific knowledge (e.g., disease prediction etc.) Active Learning (AL) is one of the solution to reduce data labeling effort. Specifically, the learning algorithm actively selects data points that provide more information for the model, hence a better model can be achieved with less labeled data. While traditional AL strategies do achieve good performance, it requires a small amount of labeled data as initialization and performs data selection in multi-round, which pose great challenge to its application, as there is no platform provide timely online interaction with data labeler and the interaction is often time inefficient. To deal with the limitations, we first propose DULO which a new setting of AL is studied: data selection is only allowed to be performed once. To further broaden the application of our method, we propose D²ULO which is built upon DULO and Domain Adaptation techniques to avoid the use of initial labeled data. Our experiments show that both of the proposed two frameworks achieve better performance compared with state-of-the-art baselines.
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Dvornik, Mikita. "Learning with Limited Annotated Data for Visual Understanding". Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAM050.

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La capacité des méthodes d'apprentissage profond à exceller en vision par ordinateur dépend fortement de la quantité de données annotées disponibles pour la formation. Pour certaines tâches, l'annotation peut être trop coûteuse et demander trop de travail, devenant ainsi le principal obstacle à une meilleure précision. Les algorithmes qui apprennent automatiquement à partir des données, sans supervision humaine, donnent de bien pires résultats que leurs homologues entièrement supervisés. Il y a donc une forte motivation à travailler sur des méthodes efficaces d'apprentissage avec des annotations limitées. Cette thèse propose d'exploiter les connaissances préalables sur la tâche et développe des solutions plus efficaces pour la compréhension des scènes et la classification de quelques images.Les principaux défis de la compréhension des scènes comprennent la détection d'objets, la sémantique et la segmentation des instances. De même, toutes ces tâches visent à reconnaître et localiser des objets, au niveau de la région ou au niveau plus précis des pixels, ce qui rend le processus d'annotation difficile. La première contribution de ce manuscrit est un réseau neuronal convolutionnel (CNN) qui effectue à la fois la détection d'objets et la segmentation sémantique. Nous concevons une architecture de réseau spécialisée, qui est formée pour résoudre les deux problèmes en un seul passage et qui fonctionne en temps réel. Grâce à la procédure de formation multitâche, les deux tâches bénéficient l'une de l'autre en termes de précision, sans données supplémentaires étiquetées.La deuxième contribution introduit une nouvelle technique d'augmentation des données, c'est-à-dire l'augmentation artificielle de la quantité de données de formation. Il vise à créer de nouvelles scènes par copier-coller d'objets d'une image à l'autre, dans un ensemble de données donné. Placer un objet dans un contexte approprié s'est avéré crucial pour améliorer la compréhension de la scène. Nous proposons de modéliser explicitement le contexte visuel à l'aide d'un CNN qui découvre les corrélations entre les catégories d'objets et leur voisinage typique, puis propose des emplacements réalistes à augmenter. Dans l'ensemble, le collage d'objets aux "bons endroits" permet d'améliorer les performances de détection et de segmentation des objets, avec des gains plus importants dans les scénarios d'annotations limitées.Pour certains problèmes, les données sont extrêmement rares et un algorithme doit apprendre de nouveaux concepts à partir de quelques exemples. Peu de classification consiste à apprendre un modèle prédictif capable de s'adapter efficacement à une nouvelle classe, avec seulement quelques échantillons annotés. Alors que la plupart des méthodes actuelles se concentrent sur le mécanisme d'adaptation, peu de travaux ont abordé explicitement le problème du manque de données sur la formation. Dans notre troisième article, nous montrons qu'en s'attaquant à la question fondamentale de la variance élevée des classificateurs d'apprentissage à faible tir, il est possible de surpasser considérablement les techniques existantes plus sophistiquées. Notre approche consiste à concevoir un ensemble de réseaux profonds pour tirer parti de la variance des classificateurs et à introduire de nouvelles stratégies pour encourager les réseaux à coopérer, tout en encourageant la diversité des prédictions. En faisant correspondre différentes sorties de réseaux sur des images d'entrée similaires, nous améliorons la précision et la robustesse du modèle par rapport à la formation d'ensemble classique. De plus, un seul réseau obtenu par distillation montre des performances similaires à celles de l'ensemble complet et donne des résultats à la pointe de la technologie, sans surcharge de calcul au moment du test
The ability of deep-learning methods to excel in computer vision highly depends on the amount of annotated data available for training. For some tasks, annotation may be too costly and labor intensive, thus becoming the main obstacle to better accuracy. Algorithms that learn from data automatically, without human supervision, perform substantially worse than their fully-supervised counterparts. Thus, there is a strong motivation to work on effective methods for learning with limited annotations. This thesis proposes to exploit prior knowledge about the task and develops more effective solutions for scene understanding and few-shot image classification.Main challenges of scene understanding include object detection, semantic and instance segmentation. Similarly, all these tasks aim at recognizing and localizing objects, at region- or more precise pixel-level, which makes the annotation process difficult. The first contribution of this manuscript is a Convolutional Neural Network (CNN) that performs both object detection and semantic segmentation. We design a specialized network architecture, that is trained to solve both problems in one forward pass, and operates in real-time. Thanks to the multi-task training procedure, both tasks benefit from each other in terms of accuracy, with no extra labeled data.The second contribution introduces a new technique for data augmentation, i.e., artificially increasing the amount of training data. It aims at creating new scenes by copy-pasting objects from one image to another, within a given dataset. Placing an object in a right context was found to be crucial in order to improve scene understanding performance. We propose to model visual context explicitly using a CNN that discovers correlations between object categories and their typical neighborhood, and then proposes realistic locations for augmentation. Overall, pasting objects in ``right'' locations allows to improve object detection and segmentation performance, with higher gains in limited annotation scenarios.For some problems, the data is extremely scarce, and an algorithm has to learn new concepts from a handful of examples. Few-shot classification consists of learning a predictive model that is able to effectively adapt to a new class, given only a few annotated samples. While most current methods concentrate on the adaptation mechanism, few works have tackled the problem of scarce training data explicitly. In our third contribution, we show that by addressing the fundamental high-variance issue of few-shot learning classifiers, it is possible to significantly outperform more sophisticated existing techniques. Our approach consists of designing an ensemble of deep networks to leverage the variance of the classifiers, and introducing new strategies to encourage the networks to cooperate, while encouraging prediction diversity. By matching different networks outputs on similar input images, we improve model accuracy and robustness, comparing to classical ensemble training. Moreover, a single network obtained by distillation shows similar to the full ensemble performance and yields state-of-the-art results with no computational overhead at test time
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Moskvyak, Olga. "Learning from limited annotated data for re-identification problem". Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/226866/1/Olga_Moskvyak_Thesis.pdf.

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The project develops machine learning methods for the re-identification task, which is matching images from the same category in a database. The thesis proposes approaches to reduce the influence of two critical challenges in image re-identification: pose variations that affect the appearance of objects and the need to annotate a large dataset to train a neural network. Depending on the domain, these challenges occur to a different extent. Our approach demonstrates superior performance on several benchmarks for people, cars, and animal categories.
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Xian, Yongqin [Verfasser]. "Learning from limited labeled data - Zero-Shot and Few-Shot Learning / Yongqin Xian". Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2020. http://d-nb.info/1219904457/34.

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Eriksson, Håkan. "Clustering Generic Log Files Under Limited Data Assumptions". Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189642.

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Complex computer systems are often prone to anomalous or erroneous behavior, which can lead to costly downtime as the systems are diagnosed and repaired. One source of information for diagnosing the errors and anomalies are log files, which are often generated in vast and diverse amounts. However, the log files' size and semi-structured nature makes manual analysis of log files generally infeasible. Some automation is desirable to sift through the log files to find the source of the anomalies or errors. This project aimed to develop a generic algorithm that could cluster diverse log files in accordance to domain expertise. The results show that the developed algorithm performs well in accordance to manual clustering even under more relaxed data assumptions.
Komplexa datorsystem är ofta benägna att uppvisa anormalt eller felaktigt beteende, vilket kan leda till kostsamma driftstopp under tiden som systemen diagnosticeras och repareras. En informationskälla till feldiagnosticeringen är loggfiler, vilka ofta genereras i stora mängder och av olika typer. Givet loggfilernas storlek och semistrukturerade utseende så blir en manuell analys orimlig att genomföra. Viss automatisering är önsvkärd för att sovra bland loggfilerna så att källan till felen och anormaliteterna blir enklare att upptäcka. Det här projektet syftade till att utveckla en generell algoritm som kan klustra olikartade loggfiler i enlighet med domänexpertis. Resultaten visar att algoritmen presterar väl i enlighet med manuell klustring även med färre antaganden om datan.
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Boman, Jimmy. "A deep learning approach to defect detection with limited data availability". Thesis, Umeå universitet, Institutionen för fysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-173207.

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In industrial processes, products are often visually inspected for defects inorder to verify their quality. Many automated visual inspection algorithms exist, and in many cases humans still perform the inspections. Advances in machine learning have showed that deep learning methods lie at the forefront of reliability and accuracy in such inspection tasks. In order to detect defects, most deep learning methods need large amounts of training data to learn from. This makes demonstrating such methods to a new customer problematic, since such data often does not exist beforehand, and has to be gathered specifically for the task. The aim of this thesis is to develop a method to perform such demonstrations. With access to only a small dataset, the method should be able to analyse an image and return a map of binary values, signifying which pixels in the original image belong to a defect and which do not. A method was developed that divides an image into overlapping patches, and analyses each patch individually for defects, using a deep learning method. Three different deep learning methods for classifying the patches were evaluated; a convolutional neural network, a transfer learning model based on the VGG19 network, and an autoencoder. The three methods were first compared in a simple binary classification task, without the patching method. They were then tested together with the patching method on two sets of images. The transfer learning model was able to identify every defect across both tests, having been trained using only four training images, proving that defect detection with deep learning can be done successfully even when there is not much training data available.
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Guo, Zhenyu. "Data famine in big data era : machine learning algorithms for visual object recognition with limited training data". Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/46412.

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Big data is an increasingly attractive concept in many fields both in academia and in industry. The increasing amount of information actually builds an illusion that we are going to have enough data to solve all the data driven problems. Unfortunately it is not true, especially for areas where machine learning methods are heavily employed, since sufficient high-quality training data doesn't necessarily come with the big data, and it is not easy or sometimes impossible to collect sufficient training samples, which most computational algorithms depend on. This thesis mainly focuses on dealing situations with limited training data in visual object recognition, by developing novel machine learning algorithms to overcome the limited training data difficulty. We investigate three issues in object recognition involving limited training data: 1. one-shot object recognition, 2. cross-domain object recognition, and 3. object recognition for images with different picture styles. For Issue 1, we propose an unsupervised feature learning algorithm by constructing a deep structure of the stacked Hierarchical Dirichlet Process (HDP) auto-encoder, in order to extract "semantic" information from unlabeled source images. For Issue 2, we propose a Domain Adaptive Input-Output Kernel Learning algorithm to reduce the domain shifts in both input and output spaces. For Issue 3, we introduce a new problem involving images with different picture styles, successfully formulate the relationship between pixel mapping functions with gradient based image descriptors, and also propose a multiple kernel based algorithm to learn an optimal combination of basis pixel mapping functions to improve the recognition accuracy. For all the proposed algorithms, experimental results on publicly available data sets demonstrate the performance improvements over previous state-of-arts.
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Ayllon, Clemente Irene [Verfasser]. "Towards natural speech acquisition: incremental word learning with limited data / Irene Ayllon Clemente". Bielefeld : Universitätsbibliothek Bielefeld, 2013. http://d-nb.info/1077063458/34.

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Chang, Fengming. "Learning accuracy from limited data using mega-fuzzification method to improve small data set learning accuracy for early flexible manufacturing system scheduling". Saarbrücken VDM Verlag Dr. Müller, 2005. http://d-nb.info/989267156/04.

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Tania, Zannatun Nayem. "Machine Learning with Reconfigurable Privacy on Resource-Limited Edge Computing Devices". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292105.

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Distributed computing allows effective data storage, processing and retrieval but it poses security and privacy issues. Sensors are the cornerstone of the IoT-based pipelines, since they constantly capture data until it can be analyzed at the central cloud resources. However, these sensor nodes are often constrained by limited resources. Ideally, it is desired to make all the collected data features private but due to resource limitations, it may not always be possible. Making all the features private may cause overutilization of resources, which would in turn affect the performance of the whole system. In this thesis, we design and implement a system that is capable of finding the optimal set of data features to make private, given the device’s maximum resource constraints and the desired performance or accuracy of the system. Using the generalization techniques for data anonymization, we create user-defined injective privacy encoder functions to make each feature of the dataset private. Regardless of the resource availability, some data features are defined by the user as essential features to make private. All other data features that may pose privacy threat are termed as the non-essential features. We propose Dynamic Iterative Greedy Search (DIGS), a greedy search algorithm that takes the resource consumption for each non-essential feature as input and returns the most optimal set of non-essential features that can be private given the available resources. The most optimal set contains the features which consume the least resources. We evaluate our system on a Fitbit dataset containing 17 data features, 4 of which are essential private features for a given classification application. Our results show that we can provide 9 additional private features apart from the 4 essential features of the Fitbit dataset containing 1663 records. Furthermore, we can save 26:21% memory as compared to making all the features private. We also test our method on a larger dataset generated with Generative Adversarial Network (GAN). However, the chosen edge device, Raspberry Pi, is unable to cater to the scale of the large dataset due to insufficient resources. Our evaluations using 1=8th of the GAN dataset result in 3 extra private features with up to 62:74% memory savings as compared to all private data features. Maintaining privacy not only requires additional resources, but also has consequences on the performance of the designed applications. However, we discover that privacy encoding has a positive impact on the accuracy of the classification model for our chosen classification application.
Distribuerad databehandling möjliggör effektiv datalagring, bearbetning och hämtning men det medför säkerhets- och sekretessproblem. Sensorer är hörnstenen i de IoT-baserade rörledningarna, eftersom de ständigt samlar in data tills de kan analyseras på de centrala molnresurserna. Dessa sensornoder begränsas dock ofta av begränsade resurser. Helst är det önskvärt att göra alla insamlade datafunktioner privata, men på grund av resursbegränsningar kanske det inte alltid är möjligt. Att göra alla funktioner privata kan orsaka överutnyttjande av resurser, vilket i sin tur skulle påverka prestanda för hela systemet. I denna avhandling designar och implementerar vi ett system som kan hitta den optimala uppsättningen datafunktioner för att göra privata, med tanke på begränsningar av enhetsresurserna och systemets önskade prestanda eller noggrannhet. Med hjälp av generaliseringsteknikerna för data-anonymisering skapar vi användardefinierade injicerbara sekretess-kodningsfunktioner för att göra varje funktion i datasetet privat. Oavsett resurstillgänglighet definieras vissa datafunktioner av användaren som viktiga funktioner för att göra privat. Alla andra datafunktioner som kan utgöra ett integritetshot kallas de icke-väsentliga funktionerna. Vi föreslår Dynamic Iterative Greedy Search (DIGS), en girig sökalgoritm som tar resursförbrukningen för varje icke-väsentlig funktion som inmatning och ger den mest optimala uppsättningen icke-väsentliga funktioner som kan vara privata med tanke på tillgängliga resurser. Den mest optimala uppsättningen innehåller de funktioner som förbrukar minst resurser. Vi utvärderar vårt system på en Fitbit-dataset som innehåller 17 datafunktioner, varav 4 är viktiga privata funktioner för en viss klassificeringsapplikation. Våra resultat visar att vi kan erbjuda ytterligare 9 privata funktioner förutom de 4 viktiga funktionerna i Fitbit-datasetet som innehåller 1663 poster. Dessutom kan vi spara 26; 21% minne jämfört med att göra alla funktioner privata. Vi testar också vår metod på en större dataset som genereras med Generative Adversarial Network (GAN). Den valda kantenheten, Raspberry Pi, kan dock inte tillgodose storleken på den stora datasetet på grund av otillräckliga resurser. Våra utvärderingar med 1=8th av GAN-datasetet resulterar i 3 extra privata funktioner med upp till 62; 74% minnesbesparingar jämfört med alla privata datafunktioner. Att upprätthålla integritet kräver inte bara ytterligare resurser utan har också konsekvenser för de designade applikationernas prestanda. Vi upptäcker dock att integritetskodning har en positiv inverkan på noggrannheten i klassificeringsmodellen för vår valda klassificeringsapplikation.
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Libros sobre el tema "Learning with Limited Data"

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Zamzmi, Ghada, Sameer Antani, Ulas Bagci, Marius George Linguraru, Sivaramakrishnan Rajaraman y Zhiyun Xue, eds. Medical Image Learning with Limited and Noisy Data. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16760-7.

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Xue, Zhiyun, Sameer Antani, Ghada Zamzmi, Feng Yang, Sivaramakrishnan Rajaraman, Sharon Xiaolei Huang, Marius George Linguraru y Zhaohui Liang, eds. Medical Image Learning with Limited and Noisy Data. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44917-8.

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Fisher, Doug y Hans-J. Lenz, eds. Learning from Data. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-2404-4.

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Big learning data. Alexandria, VA: ASTD Press, 2014.

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Hartemink, Alfred E., Alex McBratney y Maria de Lourdes Mendonça-Santos, eds. Digital Soil Mapping with Limited Data. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8592-5.

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1964-, Hartemink Alfred E., McBratney A. B y Mendonça-Santos Maria de Lourdes, eds. Digital soil mapping with limited data. Dordrecht: Springer, 2008.

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Velleman, Paul F. Learning data analysis with Data desk. New York: W.H. Freeman, 1993.

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Learning data analysis with Data desk. New York: W.H. Freeman, 1989.

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VNU Entertainment Media UK Limited and Book Data Limited: A report on the acquisition by VNU Entertainment Media UK Limited of Book Data Limited. London: Stationery Office, 2003.

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Dean, Jared. Big Data, Data Mining, and Machine Learning. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118691786.

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Capítulos de libros sobre el tema "Learning with Limited Data"

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Bennett, James, Kitty Kautzer y Leila Casteel. "Analyzing question items with limited data". En Data Analytics and Adaptive Learning, 230–41. New York: Routledge, 2023. http://dx.doi.org/10.4324/9781003244271-16.

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He, Xiangyu y Jian Cheng. "Learning Compression from Limited Unlabeled Data". En Computer Vision – ECCV 2018, 778–95. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01246-5_46.

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Chen, Shurui, Yufu Chen, Yuyin Lu, Yanghui Rao, Haoran Xie y Qing Li. "Chinese Word Embedding Learning with Limited Data". En Web and Big Data, 211–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85896-4_18.

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Wang, Li-C. "Learning from Limited Data in VLSI CAD". En Machine Learning in VLSI Computer-Aided Design, 375–99. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04666-8_13.

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Chan, Yung-Chieh, Jerry Zhang, Katie Frizzi, Nigel Calcutt y Garrison Cottrell. "Automated Skin Biopsy Analysis with Limited Data". En Medical Image Learning with Limited and Noisy Data, 229–38. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16760-7_22.

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Ramlan, Fitria Wulandari y James McDermott. "Genetic Programming with Synthetic Data for Interpretable Regression Modelling and Limited Data". En Machine Learning, Optimization, and Data Science, 142–57. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53969-5_12.

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Vu, Tu Thanh, Giang Binh Tran y Son Bao Pham. "Learning to Simplify Children Stories with Limited Data". En Intelligent Information and Database Systems, 31–41. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05476-6_4.

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Nguyen, Minh-Tien, Viet-Anh Phan, Le Thai Linh, Nguyen Hong Son, Le Tien Dung, Miku Hirano y Hajime Hotta. "Transfer Learning for Information Extraction with Limited Data". En Communications in Computer and Information Science, 469–82. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6168-9_38.

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Jain, Sanjay y Efim Kinber. "On Learning Languages from Positive Data and a Limited Number of Short Counterexamples". En Learning Theory, 259–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11776420_21.

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Liu, Alex X. y Rui Li. "Differentially Private and Budget Limited Bandit Learning over Matroids". En Algorithms for Data and Computation Privacy, 347–82. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58896-0_13.

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Actas de conferencias sobre el tema "Learning with Limited Data"

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Malaviya, Maya, Ilia Sucholutsky y Thomas L. Griffiths. "Pushing the Limits of Learning from Limited Data". En 2023 Conference on Cognitive Computational Neuroscience. Oxford, United Kingdom: Cognitive Computational Neuroscience, 2023. http://dx.doi.org/10.32470/ccn.2023.1583-0.

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Yang, Diyi, Ankur Parikh y Colin Raffel. "Learning with Limited Text Data". En Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.acl-tutorials.5.

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Khoshgoftaar, Taghi M., Chris Seiffert, Jason Van Hulse, Amri Napolitano y Andres Folleco. "Learning with limited minority class data". En Sixth International Conference on Machine Learning and Applications (ICMLA 2007). IEEE, 2007. http://dx.doi.org/10.1109/icmla.2007.76.

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Chang, Shiyu, Charu C. Aggarwal y Thomas S. Huang. "Learning Local Semantic Distances with Limited Supervision". En 2014 IEEE International Conference on Data Mining (ICDM). IEEE, 2014. http://dx.doi.org/10.1109/icdm.2014.114.

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Self, Ryan, S. M. Nahid Mahmud, Katrine Hareland y Rushikesh Kamalapurkar. "Online inverse reinforcement learning with limited data". En 2020 59th IEEE Conference on Decision and Control (CDC). IEEE, 2020. http://dx.doi.org/10.1109/cdc42340.2020.9303883.

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Chen, Hanlin y Peng Cao. "Deep Learning Based Data Augmentation and Classification for Limited Medical Data Learning". En 2019 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). IEEE, 2019. http://dx.doi.org/10.1109/icpics47731.2019.8942411.

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Lee, Kyungjae, Sunghyun Park, Hojae Han, Jinyoung Yeo, Seung-won Hwang y Juho Lee. "Learning with Limited Data for Multilingual Reading Comprehension". En Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-1283.

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Liu, Feng, Fengzhan Tian y Qiliang Zhu. "Ensembling Bayesian network structure learning on limited data". En the sixteenth ACM conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1321440.1321577.

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Mitchell, Frost, Aniqua Baset, Neal Patwari, Sneha Kumar Kasera y Aditya Bhaskara. "Deep Learning-based Localization in Limited Data Regimes". En WiSec '22: 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3522783.3529529.

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Iosifidis, Vasileios y Eirini Ntoutsi. "Large Scale Sentiment Learning with Limited Labels". En KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3097983.3098159.

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Informes sobre el tema "Learning with Limited Data"

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Safta, Cosmin, Kookjin Lee y Jaideep Ray. Predictive Skill of Deep Learning Models Trained on Limited Sequence Data. Office of Scientific and Technical Information (OSTI), octubre de 2020. http://dx.doi.org/10.2172/1688570.

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Rhoades, Alan y Ankur Mahesh. Title:Machine learning to generate gridded extreme precipitation data sets for global land areas with limited in situ measurements. Office of Scientific and Technical Information (OSTI), abril de 2021. http://dx.doi.org/10.2172/1769784.

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Cassity, Elizabeth y Debbie Wong. Teacher development multi-year studies. Insights on the challenges of data availability for measuring and reporting on student learning outcomes. Australian Council for Educational Research, 2022. http://dx.doi.org/10.37517/978-1-74286-677-2.

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Student learning outcomes are an important source of evidence regarding improved teaching quality. A multi-year teacher development study series was commissioned by the Australian Government’s Department of Foreign Affairs and Trade (DFAT) to investigate teacher development initiatives in Lao People’s Democratic Republic (Laos), Timor-Leste and Vanuatu. The overall aim of the study series is to understand the extent to which the Australian investment has improved teaching quality and student learning. This paper outlines the different approaches to sourcing and using data in each country context, and then presents initial insights about the challenges associated with the limited availability of data for measuring and reporting student learning outcomes, as a measure of teacher effectiveness. It presents key lessons learned about conducting research with limited existing student learning outcomes data and offers some solutions to inform programs in other contexts.
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Sukumar, Sreenivas R. y Carlos Emilio Del-Castillo-Negrete. Machine Learning for Big Data: A Study to Understand Limits at Scale. Office of Scientific and Technical Information (OSTI), diciembre de 2015. http://dx.doi.org/10.2172/1234336.

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Choquette, Gary. PR-000-16209-WEB Data Management Best Practices Learned from CEPM. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), abril de 2019. http://dx.doi.org/10.55274/r0011568.

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DATE: Wednesday, May 1, 2019 TIME: 2:00 - 3:30 p.m. ET PRESENTER: Gary Choquette, PRCI CLICK DOWNLOAD/BUY TO ACCESS THE REGISTRATION LINK FOR THIS WEBINAR Systems that manage large sets of data are becoming more common in the energy transportation industry. Having access to the data offers the opportunity to learn from previous experiences to help efficiently manage the future. But how does one manage to digest copious quantities of data to find nuggets within the ore? This webinar will outline some of the data management best practices learned from the research projects associated with CEPM. - Logging/capturing data tips - Techniques to identify 'bad' data - Methods of mapping equipment and associated regressions - Tips for pre-processing data for regressions - Machine learning tips - Establishing alarm limits - Identifying equipment problems - Multiple case studies Who Should Attend? - Data analysts - Equipment support specialists - Those interested in learning more about 'big data' and 'machine learning' Recommended Pre-reading: - PR-309-11202-R01 Field Demonstration Test of Advanced Engine and Compressor Diagnostics for CORE - PR-312-12210-R01 CEPM Monitoring Plan for 2SLB Reciprocating Engines* - PR-309-13208-R01 Field Demonstration of Integrated System and Expert Level Continuous Performance Monitoring for CORE* - PR-309-14209-R01 Field Demo of Integrated Expert Level Continuous Performance Monitoring - PR-309-15205-R01 Continuous Engine Performance Monitoring Technical Specification - PR-000-15208-R01 Reciprocating Engine Speed Stability as a Measure of Combustion Stability - PR-309-15209-R01 Evaluation of NSCR Specific Models for Use in CEPM - PR-000-16209-R01 Demonstration of Continuous Equipment Performance Monitoring - PR-015-17606-Z02 Elbow Meter Test Results* *Documents available to PRCI member only Attendance will be limited to the first 500 registrants to join the webinar. All remaining registrants will receive a link to view the recording after the webinar. Not able to attend? Register anyway to automatically receive a link to the recording after the webinar to view at your convenience! After registering, you will receive a confirmation email containing information about joining the webinar. Please visit our website for other webinars that may be of interest to you!
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Ogenyi, Moses. Looking back on Nigeria’s COVID-19 School Closures: Effects of Parental Investments on Learning Outcomes and Avoidance of Hysteresis in Education. Research on Improving Systems of Education (RISE), marzo de 2022. http://dx.doi.org/10.35489/bsg-rise-ri_2022/040.

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In this Insight Note, we explore how COVID-19 and related school closures impacted Nigerian schools, parents, and students. National data collected by the National Bureau of Statistics in 2020 through a monthly phone survey show that children had extremely limited contact with the education system during this time, and that families preferred low-cost alternatives such as in-home tutoring and increased parental involvement in education to e-learning tools. Additional data collected by the RISE Nigeria Team in a survey of 73 low-cost private schools in Abuja suggest that some schools did maintain contact with students during mandated school closures, that students experienced absolute learning losses equivalent to about 5-6 months of school missed in other contexts (Cooper et al, 1996), despite participation in alternative learning activities, and that the pandemic led to severe financial hardships for schools and teachers.
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Asgedom, Amare, Shelby Carvalho y Pauline Rose. Negotiating Equity: Examining Priorities, Ownership, and Politics Shaping Ethiopia’s Large-Scale Education Reforms for Equitable Learning. Research on Improving Systems of Education (RISE), marzo de 2020. http://dx.doi.org/10.35489/bsg-rise-wp_2021/067.

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In 2018, the Government of Ethiopia committed to large-scale, donor-supported reforms aimed at improving equitable learning in the basic education system—the General Education Quality Improvement Program for Equity (GEQIP-E). In this paper, we examine the reform design process in the context of Ethiopia’s political environment as a strong developmental state, assessing the influence of different stakeholder priorities which have led to the focus on equity within the quality reforms. Drawing on qualitative data from 81 key informant interviews with federal and regional government officials and donors, we explore the negotiation and power dynamics which have shaped the design of the reforms. We find that a legacy of moderately successful reforms, and a shared commitment to global goals, paved the way for negotiations of more complex and ambitious reforms between government actors and donors. Within government, we identify that regional governments were only tokenistically included in the reform process. Given that regions are responsible for the implementation of these reforms, their limited involvement in the design could have implications for success.
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Bergeron, Augustin, Arnaud Fournier, John Kabeya Kabeya, Gabriel Tourek y Jonathan L. Weigel. Using Machine Learning to Create a Property Tax Roll: Evidence from the City of Kananga, DR Congo'. Institute of Development Studies, octubre de 2023. http://dx.doi.org/10.19088/ictd.2023.053.

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Developing countries often lack the financial resources to provide public goods. Property taxation has been identified as a promising source of local revenue, because it is relatively efficient, captures growth in real estate value, and can be progressive. However, many low income countries do not collect property taxes effectively due to missing or incomplete property tax rolls. We use machine learning and computer vision models to construct a property tax roll in a large Congolese city. To train the algorithm and predict the value of all properties in the city, we rely on the value of 1,654 randomly chosen properties assessed by government land surveyors during in-person property appraisal visits, and property characteristics from administrative data or extracted from property photographs. The best machine learning algorithm, trained on property characteristics from administrative data, achieves a cross validated R2 of 60 per cent, and 22 per cent of the predicted values are within 20 per cent of the target value. The computer vision algorithms, trained on property picture features, perform less well, with only 9 per cent of the predicted values within 20 per cent of the target value for the best algorithm. We interpret the results as suggesting that simple machine learning methods can be used to construct a property tax roll, even in a context where information about properties is limited and the government can only collect a small number of property values using in-person property appraisal visits.
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Mahat, Marian, Vivienne Awad, Christopher Bradbeer, Chengxin Guo, Wesley Imms y Julia Morris. Furniture for Engagement. University of Melbourne, febrero de 2023. http://dx.doi.org/10.46580/124374.

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The aim of the study was to explore the impact of furniture and spatial settings on teachers and students. Drawing on a case study action research approach involving surveys, two primary schools (Frangipani and Jasmine Primary School) within the Sydney Catholic Schools were involved as case study sites. This report provides a summary of the findings of the impact of furniture and spatial settings on teacher efficacy, teacher mind frames, student learning and student engagement as well as perceptions of students on the furniture and spatial settings. In summary, teachers’ perceptions of their mind frames, student learning and engagement increased after the introduction of furniture in the prototype learning environment. For one teacher, the perception of their efficacy did not improve after the implementation of the prototype space and furniture. In terms of students’ perceptions of the furniture, a large proportion of students agreed that they enjoyed learning and are more motivated to learn because of the new furniture. At Jasmine Primary School, a fifth of students felt that they were not motivated to learn because of the new furniture. Further in-depth study is required to find out the underlying reasons for this. Key themes that emerged from the qualitative data on the furniture and spatial settings focus on characteristics of furniture that afforded comfort, improved concentration and auditory qualities, supported collaboration, and capacity for choice. These are important considerations to drive decisions in school designs and furniture purchases. The importance of good furniture in a learning space cannot be underestimated. New learning environments and furniture demand and create new possibilities for teacher practices and student learning. The findings of the study, whilst limited in its scale, provides three crucial considerations relating to the importance of prototyping, professional learning and longitudinal data. These carry ramifications for wider understanding and future research. Future inquiry in these three key areas can provide the much-needed evidence to support schools’ transition into new learning environments.
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Quak, Evert-Jan. K4D’s Work on the Indirect Impacts of COVID-19 in Low- and Middle- Income Countries. Institute of Development Studies (IDS), junio de 2021. http://dx.doi.org/10.19088/k4d.2021.093.

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This report is not an in-depth nor exhaustive analysis of the many indirect impacts of the pandemic on LMICs. The content is purely based on the requests from FCDO to the K4D services on this topic, and as such can only give an illustrative overview of the findings from these requests. Furthermore, insights are also taken from the data that K4D collects for each request based on the information provided by advisers and FCDO (e.g. purpose of the request, adviser’s cadre), hence, the data is limited to the information available to the K4D team at the time of the request and the level of details available may vary from one request to the other. The selection of relevant K4D outputs on the pandemic’s indirect impacts was based on an extensive search in the K4D repository on titles and research questions. The Annex shows all K4D outputs included in this report. The purpose of this report is to inform FCDO about some of the specifics of their requests on the indirect impacts of COVID-19, in general. This report will also be used as input for a K4D-FCDO learning event that takes place on the 6th of July 2021. During the event learning and evidence, trends will be discussed and how evidence and learning informed decision-making on policy and programming.
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