Relevant bibliographies by topics / Representation space / Latent space

Academic literature on the topic 'Representation space / Latent space'

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Published: 7 July 2024
Last updated: 7 July 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "Representation space / Latent space":

1

Gat, Itai, Guy Lorberbom, Idan Schwartz, and Tamir Hazan. "Latent Space Explanation by Intervention." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 679–87. http://dx.doi.org/10.1609/aaai.v36i1.19948.

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The success of deep neural nets heavily relies on their ability to encode complex relations between their input and their output. While this property serves to fit the training data well, it also obscures the mechanism that drives prediction. This study aims to reveal hidden concepts by employing an intervention mechanism that shifts the predicted class based on discrete variational autoencoders. An explanatory model then visualizes the encoded information from any hidden layer and its corresponding intervened representation. By the assessment of differences between the original representation and the intervened representation, one can determine the concepts that can alter the class, hence providing interpretability. We demonstrate the effectiveness of our approach on CelebA, where we show various visualizations for bias in the data and suggest different interventions to reveal and change bias.
2

Huang, Yulei, Ziping Ma, Huirong Li, and Jingyu Wang. "Dual Space Latent Representation Learning for Image Representation." Mathematics 11, no. 11 (May 31, 2023): 2526. http://dx.doi.org/10.3390/math11112526.

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Semi-supervised non-negative matrix factorization (NMF) has achieved successful results due to the significant ability of image recognition by a small quantity of labeled information. However, there still exist problems to be solved such as the interconnection information not being fully explored and the inevitable mixed noise in the data, which deteriorates the performance of these methods. To circumvent this problem, we propose a novel semi-supervised method named DLRGNMF. Firstly, dual latent space is characterized by the affinity matrix to explicitly reflect the interrelationship between data instances and feature variables, which can exploit the global interconnection information in dual space and reduce the adverse impacts caused by noise and redundant information. Secondly, we embed the manifold regularization mechanism in the dual graph to steadily retain the local manifold structure of dual space. Moreover, the sparsity and the biorthogonal condition are integrated to constrain matrix factorization, which can greatly improve the algorithm’s accuracy and robustness. Lastly, an effective alternating iterative updating method is proposed, and the model is optimized. Empirical evaluation on nine benchmark datasets demonstrates that DLRGNMF is more effective than competitive methods.
3

Jin Dai, Jin Dai, and Zhifang Zheng Jin Dai. "Disentangling Representation of Variational Autoencoders Based on Cloud Models." 電腦學刊 34, no. 6 (December 2023): 001–14. http://dx.doi.org/10.53106/199115992023123406001.

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<p>Variational autoencoder (VAE) has the problem of uninterpretable data generation process, because the features contained in the VAE latent space are coupled with each other and no mapping from the latent space to the semantic space is established. However, most existing algorithms cannot understand the data distribution features in the latent space semantically. In this paper, we propose a cloud model-based method for disentangling semantic features in VAE latent space by adding support vector machines (SVM) to feature transformations of latent variables, and we propose to use the cloud model to measure the degree of disentangling of semantic features in the latent space. The experimental results on the CelebA dataset show that the method obtains a good disentangling effect of semantic features in the latent space, which proves the effectiveness of the method from both qualitative and quantitative aspects.</p> <p>&nbsp;</p>
4

Namatēvs, Ivars, Artūrs Ņikuļins, Anda Slaidiņa, Laura Neimane, Oskars Radziņš, and Kaspars Sudars. "Towards Explainability of the Latent Space by Disentangled Representation Learning." Information Technology and Management Science 26 (November 30, 2023): 41–48. http://dx.doi.org/10.7250/itms-2023-0006.

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Deep neural networks are widely used in computer vision for image classification, segmentation and generation. They are also often criticised as “black boxes” because their decision-making process is often not interpretable by humans. However, learning explainable representations that explicitly disentangle the underlying mechanisms that structure observational data is still a challenge. To further explore the latent space and achieve generic processing, we propose a pipeline for discovering the explainable directions in the latent space of generative models. Since the latent space contains semantically meaningful directions and can be explained, we propose a pipeline to fully resolve the representation of the latent space. It consists of a Dirichlet encoder, conditional deterministic diffusion, a group-swap and a latent traversal module. We believe that this study provides an insight into the advancement of research explaining the disentanglement of neural networks in the community.
5

Toledo-Marín, J. Quetzalcóatl, and James A. Glazier. "Using deep LSD to build operators in GANs latent space with meaning in real space." PLOS ONE 18, no. 6 (June 29, 2023): e0287736. http://dx.doi.org/10.1371/journal.pone.0287736.

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Generative models rely on the idea that data can be represented in terms of latent variables which are uncorrelated by definition. Lack of correlation among the latent variable support is important because it suggests that the latent-space manifold is simpler to understand and manipulate than the real-space representation. Many types of generative model are used in deep learning, e.g., variational autoencoders (VAEs) and generative adversarial networks (GANs). Based on the idea that the latent space behaves like a vector space Radford et al. (2015), we ask whether we can expand the latent space representation of our data elements in terms of an orthonormal basis set. Here we propose a method to build a set of linearly independent vectors in the latent space of a trained GAN, which we call quasi-eigenvectors. These quasi-eigenvectors have two key properties: i) They span the latent space, ii) A set of these quasi-eigenvectors map to each of the labeled features one-to-one. We show that in the case of the MNIST image data set, while the number of dimensions in latent space is large by design, 98% of the data in real space map to a sub-domain of latent space of dimensionality equal to the number of labels. We then show how the quasi-eigenvectors can be used for Latent Spectral Decomposition (LSD). We apply LSD to denoise MNIST images. Finally, using the quasi-eigenvectors, we construct rotation matrices in latent space which map to feature transformations in real space. Overall, from quasi-eigenvectors we gain insight regarding the latent space topology.
6

Sang, Neil. "Does Time Smoothen Space? Implications for Space-Time Representation." ISPRS International Journal of Geo-Information 12, no. 3 (March 9, 2023): 119. http://dx.doi.org/10.3390/ijgi12030119.

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The continuous nature of space and time is a fundamental tenet of many scientific endeavors. That digital representation imposes granularity is well recognized, but whether it is possible to address space completely remains unanswered. This paper argues Hales’ proof of Kepler’s conjecture on the packing of hard spheres suggests the answer to be “no”, providing examples of why this matters in GIS generally and considering implications for spatio-temporal GIS in particular. It seeks to resolve the dichotomy between continuous and granular space by showing how a continuous space may be emergent over a random graph. However, the projection of this latent space into 3D/4D imposes granularity. Perhaps surprisingly, representing space and time as locally conjugate may be key to addressing a “smooth” spatial continuum. This insight leads to the suggestion of Face Centered Cubic Packing as a space-time topology but also raises further questions for spatio-temporal representation.
7

Heese, Raoul, Jochen Schmid, Michał Walczak, and Michael Bortz. "Calibrated simplex-mapping classification." PLOS ONE 18, no. 1 (January 17, 2023): e0279876. http://dx.doi.org/10.1371/journal.pone.0279876.

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We propose a novel methodology for general multi-class classification in arbitrary feature spaces, which results in a potentially well-calibrated classifier. Calibrated classifiers are important in many applications because, in addition to the prediction of mere class labels, they also yield a confidence level for each of their predictions. In essence, the training of our classifier proceeds in two steps. In a first step, the training data is represented in a latent space whose geometry is induced by a regular (n − 1)-dimensional simplex, n being the number of classes. We design this representation in such a way that it well reflects the feature space distances of the datapoints to their own- and foreign-class neighbors. In a second step, the latent space representation of the training data is extended to the whole feature space by fitting a regression model to the transformed data. With this latent-space representation, our calibrated classifier is readily defined. We rigorously establish its core theoretical properties and benchmark its prediction and calibration properties by means of various synthetic and real-world data sets from different application domains.
8

You, Cong-Zhe, Vasile Palade, and Xiao-Jun Wu. "Robust structure low-rank representation in latent space." Engineering Applications of Artificial Intelligence 77 (January 2019): 117–24. http://dx.doi.org/10.1016/j.engappai.2018.09.008.

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9

Banyay, Gregory A., and Andrew S. Wixom. "Latent space representation method for structural acoustic assessments." Journal of the Acoustical Society of America 155, no. 3_Supplement (March 1, 2024): A141. http://dx.doi.org/10.1121/10.0027092.

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When targeting structural acoustic objectives, engineering practitioners face epistemic uncertainties in the selection of optimal geometries and material distributions, particularly during early stages of the design process. Models built for simulating acoustic phenomena generally produce vector-valued output quantities of interest, such as autospectral density and frequency response functions. Given finite compute resources and time we seek computationally parsimonious ways to distill meaningful design information into actionable results from a limited set of model runs, and thus aim to use machine learning to perform model order reduction. Unlike time series data for which recurrent neural networks can learn from prior time steps to inform subsequent steps, frequency-dependent data demands a different machine learning paradigm. We thus evaluate the utility of autoencoders to represent structural acoustic results with a low dimensional latent space to enable such objectives as surrogate modeling for design optimization. We demonstrate the accuracy of autoencoder based methods of constructing a manifold representation for frequency dependent functions of varying modal density and damping, and discuss the predictive capability thereof.
10

Shrivastava, Aditya Divyakant, and Douglas B. Kell. "FragNet, a Contrastive Learning-Based Transformer Model for Clustering, Interpreting, Visualizing, and Navigating Chemical Space." Molecules 26, no. 7 (April 3, 2021): 2065. http://dx.doi.org/10.3390/molecules26072065.

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The question of molecular similarity is core in cheminformatics and is usually assessed via a pairwise comparison based on vectors of properties or molecular fingerprints. We recently exploited variational autoencoders to embed 6M molecules in a chemical space, such that their (Euclidean) distance within the latent space so formed could be assessed within the framework of the entire molecular set. However, the standard objective function used did not seek to manipulate the latent space so as to cluster the molecules based on any perceived similarity. Using a set of some 160,000 molecules of biological relevance, we here bring together three modern elements of deep learning to create a novel and disentangled latent space, viz transformers, contrastive learning, and an embedded autoencoder. The effective dimensionality of the latent space was varied such that clear separation of individual types of molecules could be observed within individual dimensions of the latent space. The capacity of the network was such that many dimensions were not populated at all. As before, we assessed the utility of the representation by comparing clozapine with its near neighbors, and we also did the same for various antibiotics related to flucloxacillin. Transformers, especially when as here coupled with contrastive learning, effectively provide one-shot learning and lead to a successful and disentangled representation of molecular latent spaces that at once uses the entire training set in their construction while allowing “similar” molecules to cluster together in an effective and interpretable way.
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You might also be interested in the extended bibliographies on the topic 'Representation space / Latent space' for particular source types:
Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "Representation space / Latent space":

1

Yao, Xu. "Latent representations for facial images and video editing." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT019.

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Apprendre à éditer des images et des vidéos de visages est un domaine particulièrement actif dans la recherche académique et industrielle. Cette thèse aborde le problème de l'édition de visages dans le cas particulier des images et des vidéos à haute résolution. Dans cette thèse, nous développons des méthodes basées sur l'apprentissage profond pour effectuer l'édition d'images faciales. Plus précisément, nous explorons la tâche en utilisant les représentations latentes obtenues à partir de deux types de réseaux neuronaux profonds : les modèles basés sur l'auto-encodage et les réseaux antagonistes génératifs (GAN). Pour chaque type de méthode, nous considérons un problème spécifique d'édition d'image et proposons une solution efficace qui surpasse l'état de l'art. La thèse comprend deux parties. Dans la partie I, nous explorons les tâches d'édition d'images via l'espace latent des autoencodeurs. Nous considérons d'abord la tâche de transfert de style entre les photos, et proposons un algorithme efficace qui est construit sur une paire de réseaux basés sur des autoencodeurs. Ensuite, nous étudions la tâche d'édition de l'âge du visage pour les images à haute résolution, en utilisant une architecture d'encodeur-décodeur. Le réseau proposé encode une image de visage en représentations de caractéristiques invariantes selon l'âge, et apprend un vecteur de modulation correspondant à un âge cible. Notre approche permet une édition fine de l'âge sur des images à haute résolution dans un seul modèle unifié.Dans la deuxième partie, nous explorons la tâche d'édition via l'espace latent des modèles antagonistes génératifs (GAN). Tout d'abord, nous considérons le problème de l'édition "démêlée" (disentangled) des attributs faciaux sur des images synthétiques et réelles, en proposant un réseau de transformation latent qui agit dans l'espace latent d'un modèle GAN pré-entraîné. Nous avons également proposé un pipeline de manipulation vidéo, afin de généraliser le résultat de l'édition aux vidéos. Deuxièmement, nous étudions le problème de l'inversion du GAN - la projection d'une image réelle dans l'espace latent d'un GAN pré-entraîné. En particulier, nous proposons un encodeur feed-forward, qui encode une image donnée en un code caractéristique et un code latent en une seule passe. L'encodeur proposé s'avère plus précis et plus stable pour l'inversion d'images et de vidéos, tout en conservant de bonnes capacités d'édition
Learning to edit facial images and videos is one of the most popular tasks in both academia and industrial research. This thesis addresses the problem of face editing for the special case of high-resolution images and videos.In this thesis, we develop deep learning-based methods to perform facial image editing. Specifically, we explore the task using the latent representations obtained from two types of deep neural networks: autoencoder-based models and generative adversarial networks. For each type of method, we consider a specific image editing problem and propose an effective solution that outperforms the state-of-the-art.The thesis contains two parts. In part I, we explore image editing tasks via the latent space of autoencoders. We first consider the style transfer task between photos and propose an effective algorithm that is built on a pair of autoencoder-based networks. Second, we study the face age editing task for high-resolution images, using an encoder-decoder architecture. The proposed network encodes a face image to age-invariant feature representations and learns a modulation vector corresponding to a target age. Our approach allows for fine-grained age editing on high-resolution images in a single unified model.In part II, we explore the editing task via the latent space of generative adversarial models (GANs). First, we consider the problem of facial attribute disentangled editing on synthetic and real images, by proposing a latent transformation network that acts in the latent space of a pre-trained GAN model. We also proposed a video manipulation pipeline, to generalize the editing result to videos. Second, we investigate the problem of GAN inversion -- the projection of a real image to the latent space of a pretrained GAN. In particular, we propose a feed-forward encoder, which encodes a given image to a feature code and a latent code in one pass. The proposed encoder is shown to be more accurate and stable for image and video inversion, meanwhile, maintaining good editing capacities
2

Prang, Mathieu. "Representation learning for symbolic music." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS489.

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Un élément clé du récent succès des modèles d'apprentissage profond de traitement du langage réside dans la capacité à apprendre des "embeddings" de mots efficaces. Ces méthodes fournissent des espaces vectoriels structurés de dimension réduite ayant des relations métriques intéressantes. Ceux-ci, à leur tour, peuvent être utilisés comme des représentations d'entrées efficaces pour traiter des tâches plus complexes. Dans cette thèse, nous nous concentrons sur la tâche d'apprentissage d'espaces "d'embedding" pour la musique polyphonique dans le domaine symbolique. Pour ce faire, nous explorons deux approches différentes.Tout d'abord, nous introduisons un modèle d'embedding basé sur un réseau convolutif avec un nouveau type de mécanisme d'attention hiérarchique auto-modulée, qui est calculé à chaque couche afin d'obtenir une vision hiérarchique de l'information musicale.Puis, nous proposons un autre système basé sur les VAE, un type d'auto-encodeur qui contraint la distribution des données de l'espace latent à être proche d'une distribution préalablement choisie. La musique polyphonique étant un type d'information complexe, le choix de la représentation d'entrée est un processus crucial. Nous introduisons donc une nouvelle représentation de données musicales symboliques, qui transforme une partition polyphonique en un signal continu.Enfin, nous montrons le potentiel de nos espaces d'embedding à travers le développement de plusieurs applications créatives utilisées pour améliorer la connaissance et l'expression musicales, à travers des tâches telles que la modification de mélodies ou l'identification de compositeurs
A key part in the recent success of deep language processing models lies in the ability to learn efficient word embeddings. These methods provide structured spaces of reduced dimensionality with interesting metric relationship properties. These, in turn, can be used as efficient input representations for handling more complex tasks. In this thesis, we focus on the task of learning embedding spaces for polyphonic music in the symbolic domain. To do so, we explore two different approaches.We introduce an embedding model based on a convolutional network with a novel type of self-modulated hierarchical attention, which is computed at each layer to obtain a hierarchical vision of musical information.Then, we propose another system based on VAEs, a type of auto-encoder that constrains the data distribution of the latent space to be close to a prior distribution. As polyphonic music information is very complex, the design of input representation is a crucial process. Hence, we introduce a novel representation of symbolic music data, which transforms a polyphonic score into a continuous signal.Finally, we show the potential of the resulting embedding spaces through the development of several creative applications used to enhance musical knowledge and expression, through tasks such as melodies modification or composer identification
3

Saund, Eric. "The Role of Knowledge in Visual Shape Representation." Thesis, Massachusetts Institute of Technology, 1988. http://hdl.handle.net/1721.1/6833.

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This report shows how knowledge about the visual world can be built into a shape representation in the form of a descriptive vocabulary making explicit the important geometrical relationships comprising objects' shapes. Two computational tools are offered: (1) Shapestokens are placed on a Scale-Space Blackboard, (2) Dimensionality-reduction captures deformation classes in configurations of tokens. Knowledge lies in the token types and deformation classes tailored to the constraints and regularities ofparticular shape worlds. A hierarchical shape vocabulary has been implemented supporting several later visual tasks in the two-dimensional shape domain of the dorsal fins of fishes.
4

Wanigasekara, Prashan. "Latent state space models for prediction." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106269.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, School of Engineering, System Design and Management Program, Engineering and Management Program, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 95-98).
In this thesis, I explore a novel algorithm to model the joint behavior of multiple correlated signals. Our chosen example is the ECG (Electrocardiogram) and ABP (Arterial Blood Pressure) signals from patients in the ICU (Intensive Care Unit). I then use the generated models to predict blood pressure levels of ICU patients based on their historical ECG and ABP signals. The algorithm used is a variant of a Hidden Markov model. The new extension is termed as the Latent State Space Copula Model. In the novel Latent State Space Copula Modelthe ECG, ABP signals are considered to be correlated and are modeled using a bivariate Gaussian copula with Weibull marginals generated by a hidden state. We assume that there are hidden patient "states" that transition from one hidden state to another driving a joint ECG-ABP behavior. We estimate the parameters of the model using a novel Gibbs sampling approach. Using this model, we generate predictors that are the state probabilities at any given time step and use them to predict a patient's future health condition. The predictions made by the model are binary and detects whether the Mean arterial pressure(MAP) is going to be above or below a certain threshold at a future time step. Towards the end of the thesis I do a comparison between the new Latent State Space Copula Model and a state of the art Classical Discrete HMM. The Latent State Space Copula Model achieves an Area Under the ROC (AUROC) curve of .7917 for 5 states while the Classical Discrete HMM achieves an AUROC of .7609 for 5 states.
by Prashan Wanigasekara.
S.M. in Engineering and Management
5

Elguendouze, Sofiane. "Explainable Artificial Intelligence approaches for Image Captioning." Electronic Thesis or Diss., Orléans, 2024. http://www.theses.fr/2024ORLE1003.

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L'évolution rapide des modèles de sous-titrage d'images, impulsée par l'intégration de techniques d'apprentissage profond combinant les modalités image et texte, a conduit à des systèmes de plus en plus complexes. Cependant, ces modèles fonctionnent souvent comme des boîtes noires, incapables de fournir des explications transparentes de leurs décisions. Cette thèse aborde l'explicabilité des systèmes de sous-titrage d'images basés sur des architectures Encodeur-Attention-Décodeur, et ce à travers quatre aspects. Premièrement, elle explore le concept d'espace latent, s'éloignant ainsi des approches traditionnelles basées sur l'espace de représentation originel. Deuxièmement, elle présente la notion de caractère décisif, conduisant à la formulation d'une nouvelle définition pour le concept d'influence/décisivité des composants dans le contexte de sous-titrage d'images explicable, ainsi qu'une approche par perturbation pour la capture du caractère décisif. Le troisième aspect vise à élucider les facteurs influençant la qualité des explications, en mettant l'accent sur la portée des méthodes d'explication. En conséquence, des variantes basées sur l'espace latent de méthodes d'explication bien établies telles que LRP et LIME ont été développées, ainsi que la proposition d'une approche d'évaluation centrée sur l'espace latent, connue sous le nom d'Ablation Latente. Le quatrième aspect de ce travail consiste à examiner ce que nous appelons la saillance et la représentation de certains concepts visuels, tels que la quantité d'objets, à différents niveaux de l'architecture de sous-titrage
The rapid advancement of image captioning models, driven by the integration of deep learning techniques that combine image and text modalities, has resulted in increasingly complex systems. However, these models often operate as black boxes, lacking the ability to provide transparent explanations for their decisions. This thesis addresses the explainability of image captioning systems based on Encoder-Attention-Decoder architectures, through four aspects. First, it explores the concept of the latent space, marking a departure from traditional approaches relying on the original representation space. Second, it introduces the notion of decisiveness, leading to the formulation of a new definition for the concept of component influence/decisiveness in the context of explainable image captioning, as well as a perturbation-based approach to capturing decisiveness. The third aspect aims to elucidate the factors influencing explanation quality, in particular the scope of explanation methods. Accordingly, latent-based variants of well-established explanation methods such as LRP and LIME have been developed, along with the introduction of a latent-centered evaluation approach called Latent Ablation. The fourth aspect of this work involves investigating what we call saliency and the representation of certain visual concepts, such as object quantity, at different levels of the captioning architecture
6

BIGGIO, MONICA. "Space in action: motor aspects of peripersonal space representation." Doctoral thesis, Università degli studi di Genova, 2018. http://hdl.handle.net/11567/929746.

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Mohanadas, Rohin. "Discerning truck stop semantics through latent space clustering." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240598.

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GPS systems have been in use for navigational purposes for almost three decades and have found their way into location tracking systems. The principal, Scania has a fleet of 300 thousand connected vehicles sending in position information. In this paper, we make use of position information sourced from Scania’s connected trucks, which have been abstracted into stops. The stop abstractions are built by coalescing raw position information on the basis of temporal and spatial thresholds and denote locations where the truck halts. We apply unsupervised machine learning approaches to try and understand the semantics behind these stops. The features of the truck stops are projected into a low dimensional latent space using deep autoencoders, and a clustering objective is then optimized in this low dimension space. The resultant clusters are found to be representative of different types of truck stops. The characterized truck stoppages can be useful for understanding the truck usage patterns as well transport hub usage statistics.
GPS-systemen för navigation har funnits i nästan tre årtionden och de kan numera även hittas i lokaliseringssystem. Scania har en flotta bestående av 300 000 uppkopplade fordon som skickar information om deras position till Scania. I den här masteruppsatsen används positionsinformationen från de Scaniafordon som klassificeras som stillastående. Denna klacificering bygger på rå positionsinformation som baserat på tid och rum inte får variera mer än vissa tröskelvärden och de beskriver därigenom platser där lastbilar har stannat. En oövervakad maskininlärningsmetod användes för att försöka förstå semantiken bakom dessa stillaståenden. Data från lastbilarna projiceras till ett lägre dimensionellt rum med hjälp av deep autoencoders och klustringen optimeras sedan fram i denna lägre dimension. Klustringen har i denna masteruppsats visat sig respresentativ för olika anledningar till stillastående lastbilar. Detta kan vara användbart för att förstå användarmönster men även förtransportsnavets användarstatistik.
8

Mathis, Alexander. "The representation of space in mammals." Diss., lmu, 2012. http://nbn-resolving.de/urn:nbn:de:bvb:19-150029.

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Pritchard, Annette. "Tourism representation, space and the power perspective." Thesis, Manchester Metropolitan University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311204.

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Kelly, Michael C. "Efficient representation of adaptable virtual auditory space." Thesis, University of York, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274510.

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Books on the topic "Representation space / Latent space":

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Maria, Balshaw, and Kennedy Liam 1946-, eds. Urban space and representation. London: Pluto Press, 2000.

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Balshaw, Maria, and Kennedy Liam. Urban space and representation. London: Pluto Press, 2000.

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Fernández, Juan A., and Javier González. Multi-Hierarchical Representation of Large-Scale Space. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9666-4.

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Barnett, Clive. Culture and democracy: Media, space, and representation. Edinburgh: Edinburgh University Press, 2003.

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Ewald, Björn Christian, and Carlos F. Noreña. The emperor and Rome: Space, representation, and ritual. Cambridge: Cambridge University Press, 2010.

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Naomi, Eilan, McCarthy Rosaleen A, and Brewer Bill, eds. Spatial representation: Problems in philosophy and psychology. Oxford [England]: Blackwell, 1993.

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Newcombe, Nora. Making space: The development of spatial representation and reasoning. Cambridge, Mass: MIT Press, 2000.

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Mishra, Lata. Representation of space and domestic interiority in contemporary fiction. New Delhi, India: Authors Press, 2015.

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David, Wilson. Inventing black-on-black violence: Discourse, space, and representation. Syracuse, N.Y: Syracuse University Press, 2005.

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Maunganidze, Langtone. Representation and Materialization of Architecture and Space in Zimbabwe. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47761-4.

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Book chapters on the topic "Representation space / Latent space":

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O’ Mahony, Niall, Anshul Awasthi, Joseph Walsh, and Daniel Riordan. "Latent Space Cartography for Geometrically Enriched Latent Spaces." In Communications in Computer and Information Science, 488–501. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_38.

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AbstractThere have been many developments in recent years on the exploitation of non-Euclidean geometry for the better representation of the relation between subgroups in datasets. Great progress has been made in this field of Disentangled Representation Learning, in leveraging information geometry divergence, manifold regularisation and geodesics to allow complex dynamics to be captured in the latent space of the representations produced. However, interpreting the high-dimensional latent spaces of the modern deep learning-based models involved is non-trivial. Therefore, in this paper, we investigate how techniques in Latent Space Cartography can be used to display abstract and representational 2D visualisations of manifolds.Additionally, we present a multi-task metric learning model to capture in its output representations as many metrics as is available in a multi-faceted fine-grained change detection dataset. We also implement an interactive visualisation tool that utilises cartographic techniques that allow dimensions and annotations of graphs to be representative of the underlying factors affecting individual scenarios the user can morph and transform to focus on an individual/sub-group to see how they are performing with respect to said metrics.
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Anandarajan, Murugan, Chelsey Hill, and Thomas Nolan. "Semantic Space Representation and Latent Semantic Analysis." In Practical Text Analytics, 77–91. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95663-3_6.

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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.

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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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Aathreya, Saandeep, and Shaun Canavan. "Expression Recognition Using a Flow-Based Latent-Space Representation." In Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges, 151–65. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37745-7_11.

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Peychev, Momchil, Anian Ruoss, Mislav Balunović, Maximilian Baader, and Martin Vechev. "Latent Space Smoothing for Individually Fair Representations." In Lecture Notes in Computer Science, 535–54. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19778-9_31.

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Polderman, Jan Willem, and Jan C. Willems. "Elimination of Latent Variables and State Space Representations." In Texts in Applied Mathematics, 201–40. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2953-5_6.

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Wang, Zhendong, Isak Samsten, Rami Mochaourab, and Panagiotis Papapetrou. "Learning Time Series Counterfactuals via Latent Space Representations." In Discovery Science, 369–84. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88942-5_29.

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Schlemper, Jo, Ozan Oktay, Wenjia Bai, Daniel C. Castro, Jinming Duan, Chen Qin, Jo V. Hajnal, and Daniel Rueckert. "Cardiac MR Segmentation from Undersampled k-space Using Deep Latent Representation Learning." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 259–67. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00928-1_30.

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López Diez, Paula, Jan Margeta, Khassan Diab, François Patou, and Rasmus R. Paulsen. "Unsupervised Classification of Congenital Inner Ear Malformations Using DeepDiffusion for Latent Space Representation." In Lecture Notes in Computer Science, 652–62. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43904-9_63.

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Betancourt, Roland. "Extended in the Imagination: The Representation of Architectural Space in Byzantium." In Architecture and Visual Culture in the Late Antique and Medieval Mediterranean, 105–24. Turnhout, Belgium: Brepols Publishers, 2021. http://dx.doi.org/10.1484/m.ama-eb.5.124437.

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Conference papers on the topic "Representation space / Latent space":

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Liu, Zitu, Jiawang Li, Yue Liu, Qun Liu, Guoyin Wang, and Yike Guo. "Interpretability Latent Space Method: Exploiting Shapley Representation to Explain Latent Space." In 2021 7th International Conference on Big Data and Information Analytics (BigDIA). IEEE, 2021. http://dx.doi.org/10.1109/bigdia53151.2021.9619687.

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Attia, Mohamed, MennattAllah H. Attia, Julie Iskander, Khaled Saleh, Darius Nahavandi, Ahmed Abobakr, Mohammed Hossny, and Saeid Nahavandi. "Fingerprint Synthesis Via Latent Space Representation." In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2019. http://dx.doi.org/10.1109/smc.2019.8914499.

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Thuruthel, Thomas George, Kieran Gilday, and Fumiya Iida. "Drift-Free Latent Space Representation for Soft Strain Sensors." In 2020 3rd IEEE International Conference on Soft Robotics (RoboSoft). IEEE, 2020. http://dx.doi.org/10.1109/robosoft48309.2020.9116021.

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Xu, Yi Tian, Yaqiao Li, and David Meger. "Human Motion Prediction Via Pattern Completion in Latent Representation Space." In 2019 16th Conference on Computer and Robot Vision (CRV). IEEE, 2019. http://dx.doi.org/10.1109/crv.2019.00016.

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Du, Sihua, Xiaoming Liu, and Guan Yang. "Latent space knowledge representation enhancement for low resource machine translation." In International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023), edited by Lin Wang and Xiaogang Liu. SPIE, 2023. http://dx.doi.org/10.1117/12.2679623.

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Dos Santos, Anderson Carlos, and Valdir Grassi. "Pedestrian Trajectory Prediction with Pose Representation and Latent Space Variables." In 2021 Latin American Robotics Symposium (LARS), 2021 Brazilian Symposium on Robotics (SBR), and 2021 Workshop on Robotics in Education (WRE). IEEE, 2021. http://dx.doi.org/10.1109/lars/sbr/wre54079.2021.9605473.

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Nerurkar, Pranav, Madhav Chandane, and Sunil Bhirud. "Representation learning for social networks using Homophily based Latent Space Model." In COINS '19: INTERNATIONAL CONFERENCE ON OMNI-LAYER INTELLIGENT SYSTEMS. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3312614.3312627.

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Bielawski, Romain, and Rufin VanRullen. "CLIP-based image captioning via unsupervised cycle-consistency in the latent space." In Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.repl4nlp-1.22.

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Egiazarian, Vage, Savva Ignatyev, Alexey Artemov, Oleg Voynov, Andrey Kravchenko, Youyi Zheng, Luiz Velho, and Evgeny Burnaev. "Latent-space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds." In 15th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009102604210428.

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Song, Dan, Carl Henrik Ek, Kai Huebner, and Danica Kragic. "Embodiment-specific representation of robot grasping using graphical models and latent-space discretization." In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011). IEEE, 2011. http://dx.doi.org/10.1109/iros.2011.6048145.

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Reports on the topic "Representation space / Latent space":

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Saiegh, Sebastián. Partisanship, Ideology, and Representation in Latin America. Inter-American Development Bank, August 2014. http://dx.doi.org/10.18235/0011656.

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This paper uses joint scaling methods and similar items from three large-scale surveys to place voters, parties and politicians from different Latin American countries on a common ideological space. Contrary to the conventional wisdom, the findings reveal that the "median" voter in Latin America is located to the left of the ideological spectrum, and that voter's ideological locations are highly correlated with their partisan attachments. The location of parties and leaders suggests that three distinctive clusters exist: one located at the left of the political spectrum, another at the center, and a third to the right. The results also indicate that legislators in Brazil, Chile, Mexico and Peru tend to be more "leftist" than their voters. The ideological drift, however, is not large enough to substantiate the claim that a representation gap exists in those countries.
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Hoff, Peter D., Adrian E. Raftery, and Mark S. Handcock. Latent Space Approaches to Social Network Analysis. Fort Belvoir, VA: Defense Technical Information Center, November 2001. http://dx.doi.org/10.21236/ada458734.

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Zhytaryuk, Maryan, and Iryna Ivanova. ANTI-RUSSIAN NARRATIVES OF YURIY SHVETS (ON THE MATERIALS OF HIS AUTHOR’S YOUTUBE CHANNEL). Ivan Franko National University of Lviv, March 2024. http://dx.doi.org/10.30970/vjo.2024.54-55.12154.

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The article considers the problem of the representation of anti-military narratives in the Internet space, namely in the YouTube channel. The focus is on the anti-military blog of Yuri Shvets (USA). The world, and especially the European media space, has undergone significant changes in the last few years, which are primarily related to innovative technologies and the war raging in Eastern Europe. Such transformations attract research attention, determine the relevance of the presented work. Attention is focused on the anti-military and anti-russian content of the American blogger of Ukrainian origin Yuriy Shvets on the materials of his author’s YouTube channel. The methodological basis of the study. Quantitative and qualitative comparison of the thematic sections of the research object – Yuriy Shvets’ YouTube blogging for the purpose of implementing the research subject – journalistic, (geo)political, conceptual analysis and generalization of the blogger’s anti-imperial and anti-military narratives. The issue of Ukraine’s victory and russia’s defeat is the most pressing issue for Ukraine’s true allies and partners. The purpose of this article is to show the pro-Ukrainian position of the American blogger Y. Shvets in his long verbal struggle with the putin regime based on anti-russian blogging during russia’s full-scale war in Ukraine. The analysis of Yuriy Shvets’ YouTube channel shows the technological possibilities of the latest media platforms, the transformation and convergence of traditional mass media. Social networks, messengers and YouTube will continue to grow in audience and influence. Keywords: Ukraine, russian federation, russia’s aggression against Ukraine, anti-russian narratives, Yuryy Shvets’ YouTube channel, blogging, review of American media, US aid, geopolitics.
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Xuping, Xie, Tang Qi, and Tang Xianzhu. Physics-assisted Latent Space Dynamics Learning for Stiff Collisional-radiative Models. Office of Scientific and Technical Information (OSTI), June 2024. http://dx.doi.org/10.2172/2377685.

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Lubold, Shane, Arun Chandrasekhar, and Tyler McCormick. Identifying the Latent Space Geometry of Network Models through Analysis of Curvature. Cambridge, MA: National Bureau of Economic Research, December 2020. http://dx.doi.org/10.3386/w28273.

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Aganj, Iman, Christophe Lenglet, and Guillermo Sapiro. ODF Maxima Extraction in Spherical Harmonic Representation via Analytical Search Space Reduction. Fort Belvoir, VA: Defense Technical Information Center, May 2010. http://dx.doi.org/10.21236/ada540656.

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Solomon, A. D., M. D. Morris, J. Martin, and M. Olszewski. Development of a simulation code for a latent heat thermal energy storage system in a space station. Office of Scientific and Technical Information (OSTI), April 1986. http://dx.doi.org/10.2172/5777340.

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Aggio, Carlos. 'Lady Leaders': The Case of Quotas for Women's Representation in Argentina. Inter-American Development Bank, July 2002. http://dx.doi.org/10.18235/0006873.

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Argentina has taken steps to increase women's participation in politics. In 1991, it established that 30% of the candidate list for the Chamber of Deputies had to be women. As a consequence of the measure, the percentage of women deputies has increased from 5.44% in 1991/93 to 27% in 1995/97. At the same time, the country was under Menem's presidency that was considered to neglect democratic institution such as Parliament. The main aim of this paper is to answer the question: Does a quota system enhance women's participation in weak democracies? The main argument is given that the numeric increase of women has occurred in a weak and or neglected Parliament, the potential achievements of the initiatives has been neutralized. Additionally, the study argues that women have begun to make their voices heard in a political space that was traditionally controlled by men and this, in itself, constitutes a remarkable achievement.
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Chervinchuk, Alina. THE CONCEPT OF ENEMY: REPRESENTATION IN THE UKRAINIAN MILITARY DOCUMENTARIES. Ivan Franko National University of Lviv, February 2021. http://dx.doi.org/10.30970/vjo.2021.49.11063.

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Research methodology. The following methods were used in this research: general scientific methods (descriptive, analysis, synthesis, comparison) and special (structural, hermeneutic, narrative, method of content analysis). We identified words related to the concept of the enemy and determined the context in which they are used by the authors of the collections Results. The formats of reflection of military reality in collections of military documentaries are investigated. It is emphasized that the authors-observers of events as professional communicators form a vision of events based on categories understandable to the audience – «own» and «others». Instead, the authors-participants go events have more creative space and pay more attention to their own emotional state and reflections. It is defined how the enemy is depicted and what place he occupies in the military reality represented by the authors. It is emphasized that the authors reflect the enemy in different ways. In particular, the authors-observers of the events tried to form a comprehensive vision of the events, and therefore paid much attention to the opposite side of the military conflict. Authors-participants of the events tend to show the enemy as a mass to be opposed. In such collections, the enemy is specified only in the presence of evidence confirming the presence of Russians or militants. Novelty. The research for the first time investigates the methods of representation of mi­litary activity in the collections of Ukrainian military documentaries. The article is devoted to the analysis of how the authors represent the enemy. Practical importance. The analysis of collections of military documentaries will allow to study the phenomenon of war and to trace the peculiarities of the authors’ representation of military reality.
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Kularatne, Dhanushka N., Subhrajit Bhattacharya, and M. Ani Hsieh. Computing Energy Optimal Paths in Time-Varying Flows. Drexel University, 2016. http://dx.doi.org/10.17918/d8b66v.

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Autonomous marine vehicles (AMVs) are typically deployed for long periods of time in the ocean to monitor different physical, chemical, and biological processes. Given their limited energy budgets, it makes sense to consider motion plans that leverage the dynamics of the surrounding flow field so as to minimize energy usage for these vehicles. In this paper, we present two graph search based methods to compute energy optimal paths for AMVs in two-dimensional (2-D) time-varying flows. The novelty of the proposed algorithms lies in a unique discrete graph representation of the 3-D configuration space spanned by the spatio-temporal coordinates. This enables a more efficient traversal through the search space, as opposed to a full search of the spatio-temporal configuration space. Furthermore, the proposed strategy results in solutions that are closer to the global optimal when compared to greedy searches through the spatial coordinates alone. We demonstrate the proposed algorithms by computing optimal energy paths around the Channel Islands in the Santa Barbara bay using time-varying flow field forecasts generated by the Regional Ocean Model System. We verify the accuracy of the computed paths by comparing them with paths computed via an optimal control formulation.

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