3 accepted papers
Large language models (LLMs) have shown remarkable ability in various language tasks, especially with their emergent in-context learning capability. Extending LLMs to incorporate visual inputs, large vision-language models (LVLMs) have shown impressive performance in tasks such as recognition and vi
Deep learning has given rise to the field of representation learning, which aims to automatically extract rich semantics from data. However, there have been several challenges in the generalization capabilities of deep learning models. Recent works have highlighted beneficial properties of causal mo…
Learning disentangled causal representations is a challenging problem that has gained significant attention recently due to its implications for extracting meaningful information for downstream tasks. In this work, we define a new notion of causal disentanglement from the perspective of independent…