Search Results - "contrastive variational autoencoder"

Autism Spectrum Disorder Classification with Interpretability in Children Based on Structural MRI Features Extracted Using Contrastive Variational Autoencoder

Authors: Ruimin Ma Ruitao Xie Yanlin Wang et al.

Source: Big Data Mining and Analytics, Vol 7, Iss 3, Pp 781-793 (2024)

Subject Terms: contrastive variational autoencoder (cvae), FOS: Computer and information sciences, Electronic computers. Computer science, Computer Vision and Pattern Recognition (cs.CV), autism spectrum disorder (asd) classification, Computer Science - Computer Vision and Pattern Recognition, neuroanatomical interpretation, QA75.5-76.95, transfer learning

Access URL: http://arxiv.org/abs/2307.00976
https://doaj.org/article/f6b9248fc5f34a758e6154165052503c

Attack detection model for BCoT based on contrastive variational autoencoder and metric learning

Authors: Chunwang Wu Xiaolei Liu Kangyi Ding et al.

Source: Journal of Cloud Computing: Advances, Systems and Applications, Vol 13, Iss 1, Pp 1-11 (2024)

Subject Terms: Attack detection, BCoT, Metric learning, Contrastive variational autoencoder, Oversample, Computer engineering. Computer hardware, TK7885-7895, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: https://doaj.org/toc/2192-113X

Access URL: https://doaj.org/article/a3738031ff1a423bbfeaeabe72621d1b

Uncovering neural substrates across Alzheimer’s disease stages using contrastive variational autoencoder

Authors: Yan Tang Chao Yang Yuqi Wang et al.

Source: Cerebral Cortex. 34

Subject Terms: Male, Aged, 80 and over, Alzheimer Disease, Neurofilament Proteins, Disease Progression, Humans, Brain, Female, Cognitive Dysfunction, Middle Aged, Magnetic Resonance Imaging, Biomarkers, Aged

Access URL: https://pubmed.ncbi.nlm.nih.gov/39363728

ContrastVAE++: Enhancing Contrastive Variational AutoEncoder Performance through Posterior Collapse Alleviation and Language Model-based Data Augmentation

Authors: Joshep Na Jonghyun Hong Jinho Kim et al.

Source: Journal of the Korean Operations Research and Management Science Society. 48:53-67

Counterfactual Explanation for Multivariate Times Series Using A Contrastive Variational Autoencoder

Authors: Todo, William Selmani, Merwann Laurent, Béatrice et al.

Contributors: Todo, William Selmani, Merwann Laurent, Béatrice et al.

Source: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :1-5

Subject Terms: [MATH] Mathematics [math], [INFO] Computer Science [cs]

Access URL: https://hal.science/hal-04772596v1
https://doi.org/10.1109/icassp49357.2023.10095789

Disorder-specific neurodynamic features in schizophrenia inferred by neurodynamic embedded contrastive variational autoencoder model

Authors: Chaoyue Ding Yuqing Sun Kunchi Li et al.

Source: Transl Psychiatry
Translational Psychiatry, Vol 14, Iss 1, Pp 1-14 (2024)

Subject Terms: Adult, Male, 0301 basic medicine, 0303 health sciences, Models, Neurological, Brain, Neurosciences. Biological psychiatry. Neuropsychiatry, Article, 03 medical and health sciences, Deep Learning, Schizophrenia, Humans, Female, RC321-571

Access URL: https://pubmed.ncbi.nlm.nih.gov/39695106
https://doaj.org/article/3fe43004525f4f5388439f6ef79598d7

Learning salient representation of crashes and near-crashes using supervised contrastive variational autoencoder

Authors: Boyu Jiang Feng Guo

Source: Accident Analysis & Prevention. 220:108148

ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation

Authors: Wang, Yu Zhang, Hengrui Liu, Zhiwei et al.

Source: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. :2056-2066

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Information Retrieval (cs.IR), Computer Science - Information Retrieval, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2209.00456

Spatially contrastive variational autoencoder for deciphering tissue heterogeneity from spatially resolved transcriptomics

Authors: Yaofeng Hu Kai Xiao Hengyu Yang et al.

Source: Brief Bioinform

Subject Terms: 0301 basic medicine, 03 medical and health sciences, Gene Expression Profiling, 0206 medical engineering, Problem Solving Protocol, Neural Networks, Computer, 02 engineering and technology, Transcriptome

Access URL: https://pubmed.ncbi.nlm.nih.gov/38324623

Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation

Authors: Zhe Xie Dong Wang Yichi Zhang et al.

Source: Proceedings of the Web Conference 2021. :449-459

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Information Retrieval (cs.IR), Computer Science - Information Retrieval, Machine Learning (cs.LG)

Access URL: http://arxiv.org/pdf/2103.10693
http://arxiv.org/abs/2103.10693
https://www2021.thewebconf.org/program/papers-program/#40
https://doi.org/10.1145/3442381.3449873
https://arxiv.org/abs/2103.10693
https://dblp.uni-trier.de/db/conf/www/www2021.html#XieLZLWD21
https://dl.acm.org/doi/pdf/10.1145/3442381.3449873

Autism Spectrum Disorder Classification in Children based on Structural MRI Features Extracted using Contrastive Variational Autoencoder

Authors: Ma, Ruimin Xie, Ruitao Wang, Yanlin et al.

Index Terms: Computer Science - Computer Vision and Pattern Recognition, text

URL: http://arxiv.org/abs/2307.00976

Autism Spectrum Disorder Classification in Children Based on Structural MRI Features Extracted Using Contrastive Variational Autoencoder

Authors: Ruimin Ma Ruitao Xie Yanlin Wang et al.

Subject Terms: 16. Peace & justice, 3. Good health

Generative Oversampling with a Contrastive Variational Autoencoder

Authors: Dai, Wangzhi Ng, Kenney Severson, Kristen et al.

Contributors: Dai, Wangzhi Ng, Kenney Severson, Kristen et al.

Source: Other repository

Subject Terms: 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

File Description: application/pdf; application/octet-stream

Access URL: https://dspace.mit.edu/bitstream/1721.1/137374.2/4/Oversampling_ICDM-1.pdf
https://dblp.uni-trier.de/db/conf/icdm/icdm2019.html#DaiNSHAS19
https://doi.org/10.1109/ICDM.2019.00020
https://hdl.handle.net/1721.1/137374

Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation

Authors: Xie, Zhe Liu, Chengxuan Zhang, Yichi et al.

Index Terms: Computer Science - Information Retrieval, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2103.10693

Generative Oversampling with a Contrastive Variational Autoencoder

Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory MIT-IBM Watson AI Lab et al.

Source: Other repository

Index Terms: Article, http://purl.org/eprint/type/ConferencePaper

URL: https://hdl.handle.net/1721.1/137374.2
10.1109/ICDM.2019.00020
Proceedings - IEEE International Conference on Data Mining, ICDM

Generative Oversampling with a Contrastive Variational Autoencoder

Authors: Dai, Wangzhi Ng, Kenney Severson, Kristen et al.

Source: Other repository

Index Terms: Article, http://purl.org/eprint/type/ConferencePaper

URL: https://hdl.handle.net/1721.1/137374
10.1109/ICDM.2019.00020
Proceedings - IEEE International Conference on Data Mining, ICDM

Contrastive Variational Autoencoder Enhances Salient Features

Authors: Abid, Abubakar Zou, James

Index Terms: Computer Science - Machine Learning, Statistics - Machine Learning, text

URL: http://arxiv.org/abs/1902.04601

Attack detection model for BCoT based on contrastive variational autoencoder and metric learning.

Authors: Wu, Chunwang Liu, Xiaolei Ding, Kangyi et al.

Source: Journal of Cloud Computing (2192-113X); 8/2/2024, Vol. 13 Issue 1, p1-11, 11p

Subject Terms: DEEP learning, INTERNET of things, CLOUD computing, REVELATION

Disorder-specific neurodynamic features in schizophrenia inferred by neurodynamic embedded contrastive variational autoencoder model.

Authors: Ding, Chaoyue Sun, Yuqing Li, Kunchi et al.

Source: Translational Psychiatry; 12/18/2024, Vol. 14 Issue 1, p1-14, 14p

Uncovering neural substrates across Alzheimer's disease stages using contrastive variational autoencoder.

Authors: Tang, Yan Yang, Chao Wang, Yuqi et al.

Source: Cerebral Cortex; Oct2024, Vol. 34 Issue 10, p1-9, 9p