Статті в журналах з теми "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 (June 28, 2022): 679–87. http://dx.doi.org/10.1609/aaai.v36i1.19948.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Chen, Man-Sheng, Ling Huang, Chang-Dong Wang, and Dong Huang. "Multi-View Clustering in Latent Embedding Space." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3513–20. http://dx.doi.org/10.1609/aaai.v34i04.5756.
ASEERVATHAM, SUJEEVAN. "A CONCEPT VECTOR SPACE MODEL FOR SEMANTIC KERNELS." International Journal on Artificial Intelligence Tools 18, no. 02 (April 2009): 239–72. http://dx.doi.org/10.1142/s0218213009000123.
Iraki, Tarek, and Norbert Link. "Generative models for capturing and exploiting the influence of process conditions on process curves." Journal of Intelligent Manufacturing 33, no. 2 (October 7, 2021): 473–92. http://dx.doi.org/10.1007/s10845-021-01846-4.
Zheng, Chuankun, Ruzhang Zheng, Rui Wang, Shuang Zhao, and Hujun Bao. "A Compact Representation of Measured BRDFs Using Neural Processes." ACM Transactions on Graphics 41, no. 2 (April 30, 2022): 1–15. http://dx.doi.org/10.1145/3490385.
Asai, Masataro, Hiroshi Kajino, Alex Fukunaga, and Christian Muise. "Classical Planning in Deep Latent Space." Journal of Artificial Intelligence Research 74 (August 9, 2022): 1599–686. http://dx.doi.org/10.1613/jair.1.13768.
Shang, Ronghua, Lujuan Wang, Fanhua Shang, Licheng Jiao, and Yangyang Li. "Dual space latent representation learning for unsupervised feature selection." Pattern Recognition 114 (June 2021): 107873. http://dx.doi.org/10.1016/j.patcog.2021.107873.
周, 翊航. "Low-Rank Representation Algorithm Based on Latent Feature Space." Computer Science and Application 11, no. 04 (2021): 1140–48. http://dx.doi.org/10.12677/csa.2021.114117.
Tan, Zhen, Xiang Zhao, Yang Fang, Bin Ge, and Weidong Xiao. "Knowledge Graph Representation via Similarity-Based Embedding." Scientific Programming 2018 (July 15, 2018): 1–12. http://dx.doi.org/10.1155/2018/6325635.
Bae, Seho, Nizam Ud Din, Hyunkyu Park, and Juneho Yi. "Exploiting an Intermediate Latent Space between Photo and Sketch for Face Photo-Sketch Recognition." Sensors 22, no. 19 (September 26, 2022): 7299. http://dx.doi.org/10.3390/s22197299.
Kim, Jaein, Juwon Lee, Ungjin Jang, Seri Lee, and Jooyoung Park. "PyTorch/Pyro Implementation for Representation of Motion in Latent Space." Journal of Korean Institute of Intelligent Systems 28, no. 6 (December 31, 2018): 558–63. http://dx.doi.org/10.5391/jkiis.2018.28.6.558.
Kirchoff, Kathryn E., Travis Maxfield, Alexander Tropsha, and Shawn M. Gomez. "SALSA: Semantically-Aware Latent Space Autoencoder." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 12 (March 24, 2024): 13211–19. http://dx.doi.org/10.1609/aaai.v38i12.29221.
Wu, Xiang, Huaibo Huang, Vishal M. Patel, Ran He, and Zhenan Sun. "Disentangled Variational Representation for Heterogeneous Face Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9005–12. http://dx.doi.org/10.1609/aaai.v33i01.33019005.
Raja, Vinayak, and 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, no. 1 (May 13, 2024): 152–62. http://dx.doi.org/10.60087/jaigs.v4i1.129.
Raja, Vinayak, and 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, no. 1 (March 30, 2024): 269–83. http://dx.doi.org/10.60087/jaigs.vol03.issue01.p283.
Raja, Vinayak, and 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, no. 1 (March 30, 2024): 371–91. http://dx.doi.org/10.60087/jaigs.v3i1.126.
Liao, Jiayu, Xiaolan Liu, and Mengying Xie. "Inductive Latent Space Sparse and Low-rank Subspace Clustering Algorithm." Journal of Physics: Conference Series 2224, no. 1 (April 1, 2022): 012124. http://dx.doi.org/10.1088/1742-6596/2224/1/012124.
Karimi Mamaghan, Amir Mohammad, Andrea Dittadi, Stefan Bauer, Karl Henrik Johansson, and Francesco Quinzan. "Diffusion-Based Causal Representation Learning." Entropy 26, no. 7 (June 28, 2024): 556. http://dx.doi.org/10.3390/e26070556.
Winter, Robin, Floriane Montanari, Andreas Steffen, Hans Briem, Frank Noé, and Djork-Arné Clevert. "Efficient multi-objective molecular optimization in a continuous latent space." Chemical Science 10, no. 34 (2019): 8016–24. http://dx.doi.org/10.1039/c9sc01928f.
Rivero, Daniel, Iván Ramírez-Morales, Enrique Fernandez-Blanco, Norberto Ezquerra, and Alejandro Pazos. "Classical Music Prediction and Composition by Means of Variational Autoencoders." Applied Sciences 10, no. 9 (April 27, 2020): 3053. http://dx.doi.org/10.3390/app10093053.
Ahmed, Taufique, and Luca Longo. "Interpreting Disentangled Representations of Person-Specific Convolutional Variational Autoencoders of Spatially Preserving EEG Topographic Maps via Clustering and Visual Plausibility." Information 14, no. 9 (September 4, 2023): 489. http://dx.doi.org/10.3390/info14090489.
Zhang, Jian, Jin Yuan, Chuanzhen Li, and Bin Li. "An Inverse Design Framework for Isotropic Metasurfaces Based on Representation Learning." Electronics 11, no. 12 (June 10, 2022): 1844. http://dx.doi.org/10.3390/electronics11121844.
Sha, Lei, and 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.
Khan, Shujaat. "Deep-Representation-Learning-Based Classification Strategy for Anticancer Peptides." Mathematics 12, no. 9 (April 27, 2024): 1330. http://dx.doi.org/10.3390/math12091330.
Bollon, Jordy, Michela Assale, Andrea Cina, Stefano Marangoni, Matteo Calabrese, Chiara Beatrice Salvemini, Jean Marc Christille, Stefano Gustincich, and Andrea Cavalli. "Investigating How Reproducibility and Geometrical Representation in UMAP Dimensionality Reduction Impact the Stratification of Breast Cancer Tumors." Applied Sciences 12, no. 9 (April 22, 2022): 4247. http://dx.doi.org/10.3390/app12094247.
Suo, Chuanzhe, Zhe Liu, Lingfei Mo, and 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.
You, Cong-Zhe, Zhen-Qiu Shu, and Hong-Hui Fan. "Non-negative sparse Laplacian regularized latent multi-view subspace clustering." Journal of Algorithms & Computational Technology 15 (January 2021): 174830262110249. http://dx.doi.org/10.1177/17483026211024904.
Bjerrum, Esben, and Boris Sattarov. "Improving Chemical Autoencoder Latent Space and Molecular De Novo Generation Diversity with Heteroencoders." Biomolecules 8, no. 4 (October 30, 2018): 131. http://dx.doi.org/10.3390/biom8040131.
Nguyễn, Tuấn, Nguyen Hai Hao, Dang Le Dinh Trang, Nguyen Van Tuan, and Cao Van Loi. "Robust anomaly detection methods for contamination network data." Journal of Military Science and Technology, no. 79 (May 19, 2022): 41–51. http://dx.doi.org/10.54939/1859-1043.j.mst.79.2022.41-51.
Hu, Dou, Lingwei Wei, Yaxin Liu, Wei Zhou, and Songlin Hu. "Structured Probabilistic Coding." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (March 24, 2024): 12491–501. http://dx.doi.org/10.1609/aaai.v38i11.29142.
Koskinopoulou, Maria, Michail Maniadakis, and Panos Trahanias. "Speed Adaptation in Learning from Demonstration through Latent Space Formulation." Robotica 38, no. 10 (October 17, 2019): 1867–79. http://dx.doi.org/10.1017/s0263574719001449.
Cahani, Ilda, and Marcus Stiemer. "Mathematical optimization and machine learning to support PCB topology identification." Advances in Radio Science 21 (December 1, 2023): 25–35. http://dx.doi.org/10.5194/ars-21-25-2023.
Tytarenko, Andrii. "Multi-step prediction in linearized latent state spaces for representation learning." System research and information technologies, no. 3 (October 30, 2022): 139–48. http://dx.doi.org/10.20535/srit.2308-8893.2022.3.09.
Liao, Chenxi, Masataka Sawayama, and Bei Xiao. "Unsupervised learning reveals interpretable latent representations for translucency perception." PLOS Computational Biology 19, no. 2 (February 8, 2023): e1010878. http://dx.doi.org/10.1371/journal.pcbi.1010878.
Xie, Haoyu, Changqi Wang, Mingkai Zheng, Minjing Dong, Shan You, Chong Fu, and Chang Xu. "Boosting Semi-Supervised Semantic Segmentation with Probabilistic Representations." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (June 26, 2023): 2938–46. http://dx.doi.org/10.1609/aaai.v37i3.25396.
Cristovao, Paulino, Hidemoto Nakada, Yusuke Tanimura, and 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.
Jang, Gye-Bong, and Sung-Bae Cho. "Feature Space Transformation for Fault Diagnosis of Rotating Machinery under Different Working Conditions." Sensors 21, no. 4 (February 18, 2021): 1417. http://dx.doi.org/10.3390/s21041417.
Kumaran, Vikram, Bradford Mott, and 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, no. 1 (October 1, 2020): 102–8. http://dx.doi.org/10.1609/aiide.v15i1.7418.
Kumaran, Vikram, Bradford Mott, and 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, no. 1 (October 1, 2020): 109–15. http://dx.doi.org/10.1609/aiide.v16i1.7485.
Chen, Zhuo, Haimei Zhao, Chaoyue Wang, Bo Yuan, and 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, no. 21 (March 24, 2024): 23458–59. http://dx.doi.org/10.1609/aaai.v38i21.30428.
Hajihassani, Omid, Omid Ardakanian, and Hamzeh Khazaei. "Anonymizing Sensor Data on the Edge: A Representation Learning and Transformation Approach." ACM Transactions on Internet of Things 3, no. 1 (February 28, 2022): 1–26. http://dx.doi.org/10.1145/3485820.