9 papers
Inductive Generalization for Robotic Manipulation
Annabella Macaluso, Haochen Zhang, Ishaan Masilamony +2
Understanding the generalization capabilities of visuomotor policies is essential in the development of capable robotic agents. Generalizable models learn structures that transfer…
Formalizing the Binding Problem
Lianghuan Huang, Yihao Li, Saeed Salehi +3
Representations of the world, arguably, contain information about features (e.g. something is blue, something is a circle) but also information about which features are part of the…
Causality Decodability, and Vice Versa: Lessons from Interpreting Counting ViTs
Lianghuan Huang, Yingshan Chang
Mechanistic interpretability seeks to uncover how internal components of neural networks give rise to predictions. A persistent challenge, however, is disentangling two often confl…
Learning Model Successors
Yingshan Chang, Yonatan Bisk
The notion of generalization has moved away from the classical one defined in statistical learning theory towards an emphasis on out-of-domain generalization (OODG). There has been…
Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matching
Yasi Zhang, Peiyu Yu, Yaxuan Zhu +4
Generative models based on flow matching have attracted significant attention for their simplicity and superior performance in high-resolution image synthesis. By leveraging the in…
DiffusionPID: Interpreting Diffusion via Partial Information Decomposition
Rushikesh Zawar, Shaurya Dewan, Prakanshul Saxena +3
Text-to-image diffusion models have made significant progress in generating naturalistic images from textual inputs, and demonstrate the capacity to learn and represent complex vis…