Relevant bibliographies by topics / Representation space / Latent space

Contents

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

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

Author: Grafiati
Published: 7 July 2024
Last updated: 31 July 2025

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

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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 (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
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Huang, Yulei, Ziping Ma, Huirong Li, and Jingyu Wang. "Dual Space Latent Representation Learning for Image Representation." Mathematics 11, no. 11 (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 d
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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 (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 clo
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Heese, Raoul, Jochen Schmid, Michał Walczak, and Michael Bortz. "Calibrated simplex-mapping classification." PLOS ONE 18, no. 1 (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 represent
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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 se
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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 (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 spac
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Sang, Neil. "Does Time Smoothen Space? Implications for Space-Time Representation." ISPRS International Journal of Geo-Information 12, no. 3 (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 o
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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 (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 bi
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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 (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 m
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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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Dissertations / Theses on the topic "Representation space / Latent space"

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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 antagonis
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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
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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 supp
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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.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 95-98).<br>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
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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 appro
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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 underst
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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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Balshaw, Maria, and Kennedy Liam. Urban space and representation. Pluto Press, 2000.

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

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Fernández, Juan A., and Javier González. Multi-Hierarchical Representation of Large-Scale Space. 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 University Press, 2003.

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Yale University. Dept. of Classics, ed. The emperor and Rome: Space, representation, and ritual. Cambridge University Press, 2010.

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

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

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G, Zilliac Gregory, Davis S. S, and Ames Research Center, eds. Flow visualization study of a two-dimensional representation of the space shuttle launch pad configuration. National Aeronautics and Space Administration, Ames Research Center, 1987.

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Mokhtarian, Farzin, and Miroslaw Bober. Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization. Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-017-0343-7.

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1965-, Bober Miroslaw Z., ed. Curvature scale space representation: Theory, applications, and MPEG-7 standardization. Kluwer Academic Publishers, 2003.

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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. 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 inve
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Anandarajan, Murugan, Chelsey Hill, and Thomas Nolan. "Semantic Space Representation and Latent Semantic Analysis." In Practical Text Analytics. 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. 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 cri
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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. 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. 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. 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. 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, et al. "Cardiac MR Segmentation from Undersampled k-space Using Deep Latent Representation Learning." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. 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. 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. 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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Castillo, Guillermo A., Bowen Weng, Wei Zhang, and Ayonga Hereid. "Data-Driven Latent Space Representation for Robust Bipedal Locomotion Learning." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610978.

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Pradeep, Chakka Sai, and Neelam Sinha. "Time-Invariant Latent Space Signatures for Enhanced Time-Frequency Domain Representation." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651195.

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Bogosavljev, Dejan, and Željko Lukač. "Image Classification of partially labeled Datasets using the Latent Space Representation of Autoencoders." In 2024 International Conference on Smart Systems and Technologies (SST). IEEE, 2024. http://dx.doi.org/10.1109/sst61991.2024.10755443.

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Oliveira-Filho, Adaiton, Ryad Zemouri, Francis Pelletier, and Antoine Tahan. "Remaining Useful Life Estimation Based on the System Trajectory in a Latent Space Representation." In 2024 Prognostics and System Health Management Conference (PHM). IEEE, 2024. http://dx.doi.org/10.1109/phm61473.2024.00075.

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Peng, Yifeng, Haoxi Zhang, Fei Li, and Edward Szczerbicki. "Chromosome Straightening via Disentangled Representations: Exploring Semantic Trajectories in GAN’s Latent Space." In 2024 5th International Conference on Computer, Big Data and Artificial Intelligence (ICCBD+AI). IEEE, 2024. https://doi.org/10.1109/iccbd-ai65562.2024.00049.

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Manor, Ofir, Ortal Lavi, Tomer Schwartz, et al. "Retrieval of Network Packets Information Using a Generated Latent Space Representation for Network Analysis in Cyber Security." In 2024 Annual Computer Security Applications Conference Workshops (ACSAC Workshops). IEEE, 2024. https://doi.org/10.1109/acsacw65225.2024.00032.

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Huang, Kuan-Che, David Shan-Hill Wong, and Yuan Yao. "Predicting Final Properties in Ibuprofen Production with Variable Batch Durations." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.146695.

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This study addresses the challenge of predicting final properties in batch processes with highly uneven durations, using the ibuprofen production process as a case study. Novel methodologies are proposed and compared against traditional regression algorithms, which rely on batch trajectory synchronization as a pre-processing step. The performance of each method is evaluated using established metrics. The data for this study were generated using Aspen Plus V12 simulation software, focused on batch reactors. To handle the unequal-length trajectories in batch processes, this research constructs a
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Lo, Yung-Fu, and Shun-Wen Hsiao. "Exploring the Semantic Representations of Text in Subspaces of Latent Space: A Case Study on Color." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825707.

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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, et al. "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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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, 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,
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Honkala, Alexander. Latent Space Journal Mission Statement. ResearchHub Technologies, Inc., 2024. http://dx.doi.org/10.55277/researchhub.sokdroqu.

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Hoff, Peter D., Adrian E. Raftery, and Mark S. Handcock. Latent Space Approaches to Social Network Analysis. Defense Technical Information Center, 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, 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
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Zhao, Xiaoquan. Latent space analysis and alignment for cross-Language code translation. Iowa State University, 2024. https://doi.org/10.31274/cc-20250502-39.

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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), 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. National Bureau of Economic Research, 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. Defense Technical Information Center, 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), 1986. http://dx.doi.org/10.2172/5777340.

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Mestre Delgado, Giselle. The Artistic Representation of the Witch: From Demonization to Empowerment. Florida International University, 2025. https://doi.org/10.25148/fiuurj.3.1.2.

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Abstract:
This journal article examines the portrayal of the witch archetype in Art History, uncovering not only the aesthetic choices made by artists but also the ways in which prevailing beliefs, moral panics, and societal anxieties have shaped these depictions. I discuss the iconography, visual imagery, literary inspirations, and historical context behind each examined artwork and artist to fully grasp this turbulent history. The term “witch” describes mythological figures with inborn or acquired magical powers. The ways these powers function differ from region and culture. However, one aspect prevai
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