1 citations · 1 across the 3 of their papers we have counts for
4 papers
JEPA-Anything: Learning Predictive Models across Different Worlds
Taoyong Cui, Zhongyao Wang, Xinyue Xu +10
World modeling enables intelligence to anticipate consequences, guide interventions, and learn from interaction. Yet predictive models remain domain-specific: can a common learning…
Seeking Necessary and Sufficient Information from Multimodal Medical Data
Boyu Chen, Weiye Bao, Junjie Liu +5
Learning multimodal representations from medical images and other data sources can provide richer information for decision-making. While various multimodal models have been develop…
Causal Representation Learning from Multimodal Biomedical Observations
Yuewen Sun, Lingjing Kong, Guangyi Chen +10
Prevalent in biomedical applications (e.g., human phenotype research), multimodal datasets can provide valuable insights into the underlying physiological mechanisms. However, curr…
Seeking the Sufficiency and Necessity Causal Features in Multimodal Representation Learning
Boyu Chen, Junjie Liu, Zhu Li +1
Probability of necessity and sufficiency (PNS) measures the likelihood of a feature set being both necessary and sufficient for predicting an outcome. It has proven effective in gu…