Artículos de revistas sobre el tema "Representation space / Latent space"
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Gat, Itai, Guy Lorberbom, Idan Schwartz y Tamir Hazan. "Latent Space Explanation by Intervention". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 1 (28 de junio de 2022): 679–87. http://dx.doi.org/10.1609/aaai.v36i1.19948.
Texto completoHuang, Yulei, Ziping Ma, Huirong Li y Jingyu Wang. "Dual Space Latent Representation Learning for Image Representation". Mathematics 11, n.º 11 (31 de mayo de 2023): 2526. http://dx.doi.org/10.3390/math11112526.
Texto completoJin Dai, Jin Dai y Zhifang Zheng Jin Dai. "Disentangling Representation of Variational Autoencoders Based on Cloud Models". 電腦學刊 34, n.º 6 (diciembre de 2023): 001–14. http://dx.doi.org/10.53106/199115992023123406001.
Texto completoNamatēvs, Ivars, Artūrs Ņikuļins, Anda Slaidiņa, Laura Neimane, Oskars Radziņš y Kaspars Sudars. "Towards Explainability of the Latent Space by Disentangled Representation Learning". Information Technology and Management Science 26 (30 de noviembre de 2023): 41–48. http://dx.doi.org/10.7250/itms-2023-0006.
Texto completoToledo-Marín, J. Quetzalcóatl y James A. Glazier. "Using deep LSD to build operators in GANs latent space with meaning in real space". PLOS ONE 18, n.º 6 (29 de junio de 2023): e0287736. http://dx.doi.org/10.1371/journal.pone.0287736.
Texto completoSang, Neil. "Does Time Smoothen Space? Implications for Space-Time Representation". ISPRS International Journal of Geo-Information 12, n.º 3 (9 de marzo de 2023): 119. http://dx.doi.org/10.3390/ijgi12030119.
Texto completoHeese, Raoul, Jochen Schmid, Michał Walczak y Michael Bortz. "Calibrated simplex-mapping classification". PLOS ONE 18, n.º 1 (17 de enero de 2023): e0279876. http://dx.doi.org/10.1371/journal.pone.0279876.
Texto completoYou, Cong-Zhe, Vasile Palade y Xiao-Jun Wu. "Robust structure low-rank representation in latent space". Engineering Applications of Artificial Intelligence 77 (enero de 2019): 117–24. http://dx.doi.org/10.1016/j.engappai.2018.09.008.
Texto completoBanyay, Gregory A. y Andrew S. Wixom. "Latent space representation method for structural acoustic assessments". Journal of the Acoustical Society of America 155, n.º 3_Supplement (1 de marzo de 2024): A141. http://dx.doi.org/10.1121/10.0027092.
Texto completoShrivastava, Aditya Divyakant y Douglas B. Kell. "FragNet, a Contrastive Learning-Based Transformer Model for Clustering, Interpreting, Visualizing, and Navigating Chemical Space". Molecules 26, n.º 7 (3 de abril de 2021): 2065. http://dx.doi.org/10.3390/molecules26072065.
Texto completoChen, Man-Sheng, Ling Huang, Chang-Dong Wang y Dong Huang. "Multi-View Clustering in Latent Embedding Space". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 3513–20. http://dx.doi.org/10.1609/aaai.v34i04.5756.
Texto completoASEERVATHAM, SUJEEVAN. "A CONCEPT VECTOR SPACE MODEL FOR SEMANTIC KERNELS". International Journal on Artificial Intelligence Tools 18, n.º 02 (abril de 2009): 239–72. http://dx.doi.org/10.1142/s0218213009000123.
Texto completoIraki, Tarek y Norbert Link. "Generative models for capturing and exploiting the influence of process conditions on process curves". Journal of Intelligent Manufacturing 33, n.º 2 (7 de octubre de 2021): 473–92. http://dx.doi.org/10.1007/s10845-021-01846-4.
Texto completoZheng, Chuankun, Ruzhang Zheng, Rui Wang, Shuang Zhao y Hujun Bao. "A Compact Representation of Measured BRDFs Using Neural Processes". ACM Transactions on Graphics 41, n.º 2 (30 de abril de 2022): 1–15. http://dx.doi.org/10.1145/3490385.
Texto completoAsai, Masataro, Hiroshi Kajino, Alex Fukunaga y Christian Muise. "Classical Planning in Deep Latent Space". Journal of Artificial Intelligence Research 74 (9 de agosto de 2022): 1599–686. http://dx.doi.org/10.1613/jair.1.13768.
Texto completoShang, Ronghua, Lujuan Wang, Fanhua Shang, Licheng Jiao y Yangyang Li. "Dual space latent representation learning for unsupervised feature selection". Pattern Recognition 114 (junio de 2021): 107873. http://dx.doi.org/10.1016/j.patcog.2021.107873.
Texto completo周, 翊航. "Low-Rank Representation Algorithm Based on Latent Feature Space". Computer Science and Application 11, n.º 04 (2021): 1140–48. http://dx.doi.org/10.12677/csa.2021.114117.
Texto completoTan, Zhen, Xiang Zhao, Yang Fang, Bin Ge y Weidong Xiao. "Knowledge Graph Representation via Similarity-Based Embedding". Scientific Programming 2018 (15 de julio de 2018): 1–12. http://dx.doi.org/10.1155/2018/6325635.
Texto completoBae, Seho, Nizam Ud Din, Hyunkyu Park y Juneho Yi. "Exploiting an Intermediate Latent Space between Photo and Sketch for Face Photo-Sketch Recognition". Sensors 22, n.º 19 (26 de septiembre de 2022): 7299. http://dx.doi.org/10.3390/s22197299.
Texto completoKim, Jaein, Juwon Lee, Ungjin Jang, Seri Lee y Jooyoung Park. "PyTorch/Pyro Implementation for Representation of Motion in Latent Space". Journal of Korean Institute of Intelligent Systems 28, n.º 6 (31 de diciembre de 2018): 558–63. http://dx.doi.org/10.5391/jkiis.2018.28.6.558.
Texto completoKirchoff, Kathryn E., Travis Maxfield, Alexander Tropsha y Shawn M. Gomez. "SALSA: Semantically-Aware Latent Space Autoencoder". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 12 (24 de marzo de 2024): 13211–19. http://dx.doi.org/10.1609/aaai.v38i12.29221.
Texto completoWu, Xiang, Huaibo Huang, Vishal M. Patel, Ran He y Zhenan Sun. "Disentangled Variational Representation for Heterogeneous Face Recognition". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 9005–12. http://dx.doi.org/10.1609/aaai.v33i01.33019005.
Texto completoRaja, Vinayak y Bhuvi Chopra. "Fostering Privacy in Collaborative Data Sharing via Auto-encoder Latent Space Embedding". Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 4, n.º 1 (13 de mayo de 2024): 152–62. http://dx.doi.org/10.60087/jaigs.v4i1.129.
Texto completoRaja, Vinayak y BHUVI chopra. "Cultivating Privacy in Collaborative Data Sharing through Auto-encoder Latent Space Embeddings". Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 3, n.º 1 (30 de marzo de 2024): 269–83. http://dx.doi.org/10.60087/jaigs.vol03.issue01.p283.
Texto completoRaja, Vinayak y Bhuvi Chopra. "Cultivating Privacy in Collaborative Data Sharing through Auto-encoder Latent Space Embeddings". Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 3, n.º 1 (30 de marzo de 2024): 371–91. http://dx.doi.org/10.60087/jaigs.v3i1.126.
Texto completoLiao, Jiayu, Xiaolan Liu y Mengying Xie. "Inductive Latent Space Sparse and Low-rank Subspace Clustering Algorithm". Journal of Physics: Conference Series 2224, n.º 1 (1 de abril de 2022): 012124. http://dx.doi.org/10.1088/1742-6596/2224/1/012124.
Texto completoKarimi Mamaghan, Amir Mohammad, Andrea Dittadi, Stefan Bauer, Karl Henrik Johansson y Francesco Quinzan. "Diffusion-Based Causal Representation Learning". Entropy 26, n.º 7 (28 de junio de 2024): 556. http://dx.doi.org/10.3390/e26070556.
Texto completoWinter, Robin, Floriane Montanari, Andreas Steffen, Hans Briem, Frank Noé y Djork-Arné Clevert. "Efficient multi-objective molecular optimization in a continuous latent space". Chemical Science 10, n.º 34 (2019): 8016–24. http://dx.doi.org/10.1039/c9sc01928f.
Texto completoRivero, Daniel, Iván Ramírez-Morales, Enrique Fernandez-Blanco, Norberto Ezquerra y Alejandro Pazos. "Classical Music Prediction and Composition by Means of Variational Autoencoders". Applied Sciences 10, n.º 9 (27 de abril de 2020): 3053. http://dx.doi.org/10.3390/app10093053.
Texto completoAhmed, Taufique y Luca Longo. "Interpreting Disentangled Representations of Person-Specific Convolutional Variational Autoencoders of Spatially Preserving EEG Topographic Maps via Clustering and Visual Plausibility". Information 14, n.º 9 (4 de septiembre de 2023): 489. http://dx.doi.org/10.3390/info14090489.
Texto completoZhang, Jian, Jin Yuan, Chuanzhen Li y Bin Li. "An Inverse Design Framework for Isotropic Metasurfaces Based on Representation Learning". Electronics 11, n.º 12 (10 de junio de 2022): 1844. http://dx.doi.org/10.3390/electronics11121844.
Texto completoSha, Lei y Thomas Lukasiewicz. "Text Attribute Control via Closed-Loop Disentanglement". Transactions of the Association for Computational Linguistics 12 (2024): 190–209. http://dx.doi.org/10.1162/tacl_a_00640.
Texto completoKhan, Shujaat. "Deep-Representation-Learning-Based Classification Strategy for Anticancer Peptides". Mathematics 12, n.º 9 (27 de abril de 2024): 1330. http://dx.doi.org/10.3390/math12091330.
Texto completoBollon, Jordy, Michela Assale, Andrea Cina, Stefano Marangoni, Matteo Calabrese, Chiara Beatrice Salvemini, Jean Marc Christille, Stefano Gustincich y Andrea Cavalli. "Investigating How Reproducibility and Geometrical Representation in UMAP Dimensionality Reduction Impact the Stratification of Breast Cancer Tumors". Applied Sciences 12, n.º 9 (22 de abril de 2022): 4247. http://dx.doi.org/10.3390/app12094247.
Texto completoSuo, Chuanzhe, Zhe Liu, Lingfei Mo y Yunhui Liu. "LPD-AE: Latent Space Representation of Large-Scale 3D Point Cloud". IEEE Access 8 (2020): 108402–17. http://dx.doi.org/10.1109/access.2020.2999727.
Texto completoYou, Cong-Zhe, Zhen-Qiu Shu y Hong-Hui Fan. "Non-negative sparse Laplacian regularized latent multi-view subspace clustering". Journal of Algorithms & Computational Technology 15 (enero de 2021): 174830262110249. http://dx.doi.org/10.1177/17483026211024904.
Texto completoBjerrum, Esben y Boris Sattarov. "Improving Chemical Autoencoder Latent Space and Molecular De Novo Generation Diversity with Heteroencoders". Biomolecules 8, n.º 4 (30 de octubre de 2018): 131. http://dx.doi.org/10.3390/biom8040131.
Texto completoNguyễn, Tuấn, Nguyen Hai Hao, Dang Le Dinh Trang, Nguyen Van Tuan y Cao Van Loi. "Robust anomaly detection methods for contamination network data". Journal of Military Science and Technology, n.º 79 (19 de mayo de 2022): 41–51. http://dx.doi.org/10.54939/1859-1043.j.mst.79.2022.41-51.
Texto completoHu, Dou, Lingwei Wei, Yaxin Liu, Wei Zhou y Songlin Hu. "Structured Probabilistic Coding". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 11 (24 de marzo de 2024): 12491–501. http://dx.doi.org/10.1609/aaai.v38i11.29142.
Texto completoKoskinopoulou, Maria, Michail Maniadakis y Panos Trahanias. "Speed Adaptation in Learning from Demonstration through Latent Space Formulation". Robotica 38, n.º 10 (17 de octubre de 2019): 1867–79. http://dx.doi.org/10.1017/s0263574719001449.
Texto completoCahani, Ilda y Marcus Stiemer. "Mathematical optimization and machine learning to support PCB topology identification". Advances in Radio Science 21 (1 de diciembre de 2023): 25–35. http://dx.doi.org/10.5194/ars-21-25-2023.
Texto completoTytarenko, Andrii. "Multi-step prediction in linearized latent state spaces for representation learning". System research and information technologies, n.º 3 (30 de octubre de 2022): 139–48. http://dx.doi.org/10.20535/srit.2308-8893.2022.3.09.
Texto completoLiao, Chenxi, Masataka Sawayama y Bei Xiao. "Unsupervised learning reveals interpretable latent representations for translucency perception". PLOS Computational Biology 19, n.º 2 (8 de febrero de 2023): e1010878. http://dx.doi.org/10.1371/journal.pcbi.1010878.
Texto completoXie, Haoyu, Changqi Wang, Mingkai Zheng, Minjing Dong, Shan You, Chong Fu y Chang Xu. "Boosting Semi-Supervised Semantic Segmentation with Probabilistic Representations". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 3 (26 de junio de 2023): 2938–46. http://dx.doi.org/10.1609/aaai.v37i3.25396.
Texto completoCristovao, Paulino, Hidemoto Nakada, Yusuke Tanimura y Hideki Asoh. "Generating In-Between Images Through Learned Latent Space Representation Using Variational Autoencoders". IEEE Access 8 (2020): 149456–67. http://dx.doi.org/10.1109/access.2020.3016313.
Texto completoJang, Gye-Bong y Sung-Bae Cho. "Feature Space Transformation for Fault Diagnosis of Rotating Machinery under Different Working Conditions". Sensors 21, n.º 4 (18 de febrero de 2021): 1417. http://dx.doi.org/10.3390/s21041417.
Texto completoKumaran, Vikram, Bradford Mott y James Lester. "Generating Game Levels for Multiple Distinct Games with a Common Latent Space". Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 15, n.º 1 (1 de octubre de 2020): 102–8. http://dx.doi.org/10.1609/aiide.v15i1.7418.
Texto completoKumaran, Vikram, Bradford Mott y James Lester. "Generating Game Levels for Multiple Distinct Games with a Common Latent Space". Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 16, n.º 1 (1 de octubre de 2020): 109–15. http://dx.doi.org/10.1609/aiide.v16i1.7485.
Texto completoChen, Zhuo, Haimei Zhao, Chaoyue Wang, Bo Yuan y Xiu Li. "Dual Mapping of 2D StyleGAN for 3D-Aware Image Generation and Manipulation (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 21 (24 de marzo de 2024): 23458–59. http://dx.doi.org/10.1609/aaai.v38i21.30428.
Texto completoHajihassani, Omid, Omid Ardakanian y Hamzeh Khazaei. "Anonymizing Sensor Data on the Edge: A Representation Learning and Transformation Approach". ACM Transactions on Internet of Things 3, n.º 1 (28 de febrero de 2022): 1–26. http://dx.doi.org/10.1145/3485820.
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