24 citations · 45 across the 6 of their papers we have counts for
6 papers
Generalizable Information Theoretic Causal Representation
Mengyue Yang, Xinyu Cai, Furui Liu +4
It is evidence that representation learning can improve model's performance over multiple downstream tasks in many real-world scenarios, such as image classification and recommende…
Contrastive ACE: Domain Generalization Through Alignment of Causal Mechanisms
Yunqi Wang, Furui Liu, Zhitang Chen +4
Domain generalization aims to learn knowledge invariant across different distributions while semantically meaningful for downstream tasks from multiple source domains, to improve t…
Causal World Models by Unsupervised Deconfounding of Physical Dynamics
Minne Li, Mengyue Yang, Furui Liu +3
The capability of imagining internally with a mental model of the world is vitally important for human cognition. If a machine intelligent agent can learn a world model to create a…
Weakly Supervised Disentangled Generative Causal Representation Learning
Xinwei Shen, Furui Liu, Hanze Dong +3
This paper proposes a Disentangled gEnerative cAusal Representation (DEAR) learning method under appropriate supervised information. Unlike existing disentanglement methods that en…
Decoder-free Robustness Disentanglement without (Additional) Supervision
Yifei Wang, Dan Peng, Furui Liu +3
Adversarial Training (AT) is proposed to alleviate the adversarial vulnerability of machine learning models by extracting only robust features from the input, which, however, inevi…
CausalVAE: Structured Causal Disentanglement in Variational Autoencoder
Mengyue Yang, Furui Liu, Zhitang Chen +3
Learning disentanglement aims at finding a low dimensional representation which consists of multiple explanatory and generative factors of the observational data. The framework of…