most citedTemporally Disentangled Representation Learning

8 citations · 13 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG2024

CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process

Guangyi Chen, Yifan Shen, Zhenhao Chen +5

Identifying the underlying time-delayed latent causal processes in sequential data is vital for grasping temporal dynamics and making downstream reasoning. While some recent method…

cs.LG2023

Temporally Disentangled Representation Learning under Unknown Nonstationarity

Xiangchen Song, Weiran Yao, Yewen Fan +5

In unsupervised causal representation learning for sequential data with time-delayed latent causal influences, strong identifiability results for the disentanglement of causally-re…

cs.LG2023

Subspace Identification for Multi-Source Domain Adaptation

Zijian Li, Ruichu Cai, Guangyi Chen +3

Multi-source domain adaptation (MSDA) methods aim to transfer knowledge from multiple labeled source domains to an unlabeled target domain. Although current methods achieve target…

q-bio.NC20225 cited

Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks

Yue Yu, Xuan Kan, Hejie Cui +9

Functional magnetic resonance imaging (fMRI) has become one of the most common imaging modalities for brain function analysis. Recently, graph neural networks (GNN) have been adopt…

cs.LG20228 cited

Temporally Disentangled Representation Learning

Weiran Yao, Guangyi Chen, Kun Zhang

Recently in the field of unsupervised representation learning, strong identifiability results for disentanglement of causally-related latent variables have been established by expl…