From Orthogonality to Dependency: Learning Disentangled Representation
for Multi-Modal Time-Series Sensing Signals

From Orthogonality to Dependency: Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals

25 May 2024

Zijian Li

Papers citing "From Orthogonality to Dependency: Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals"

7 / 7 papers shown

Title
Foundation Models for Time Series Analysis: A Tutorial and Survey Yuxuan Liang Haomin Wen Yuqi Nie Yushan Jiang Ming Jin Dongjin Song Shirui Pan Qingsong Wen AI4TS AI4CE 87 108 0 21 Mar 2024
Counterfactual Generation with Identifiability Guarantees Hanqi Yan Lingjing Kong Lin Gui Yuejie Chi Eric P. Xing Yulan He Kun Zhang CML OOD 32 7 0 23 Feb 2024
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models Goutham Rajendran Simon Buchholz Bryon Aragam Bernhard Schölkopf Pradeep Ravikumar AI4CE 83 19 0 14 Feb 2024
Causal Representation Learning from Multiple Distributions: A General Setting Kun Zhang Shaoan Xie Ignavier Ng Yujia Zheng CML OOD 18 18 0 07 Feb 2024
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects Kexin Zhang Qingsong Wen Chaoli Zhang Rongyao Cai Ming Jin ... James Y. Zhang Y. Liang Guansong Pang Dongjin Song Shirui Pan AI4TS 109 97 0 16 Jun 2023
Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning Qian Jiang Changyou Chen Han Zhao Liqun Chen Q. Ping S. D. Tran Yi Xu Belinda Zeng Trishul M. Chilimbi 41 36 0 10 Mar 2023
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 258 7,337 0 11 Nov 2021