27 citations · 59 across the 7 of their papers we have counts for
11 papers
Causal Layering via Conditional Entropy
Itai Feigenbaum, Devansh Arpit, Huan Wang +5
Causal discovery aims to recover information about an unobserved causal graph from the observable data it generates. Layerings are orderings of the variables which place causes bef…
Editing Arbitrary Propositions in LLMs without Subject Labels
Itai Feigenbaum, Devansh Arpit, Huan Wang +5
Large Language Model (LLM) editing modifies factual information in LLMs. Locate-and-Edit (L\&E) methods accomplish this by finding where relevant information is stored within the n…
Artificial Intelligence Index Report 2023
Nestor Maslej, Loredana Fattorini, Erik Brynjolfsson +11
Welcome to the sixth edition of the AI Index Report. This year, the report introduces more original data than any previous edition, including a new chapter on AI public opinion, a…
BOLAA: Benchmarking and Orchestrating LLM-augmented Autonomous Agents
Zhiwei Liu, Weiran Yao, Jianguo Zhang +12
The massive successes of large language models (LLMs) encourage the emerging exploration of LLM-augmented Autonomous Agents (LAAs). An LAA is able to generate actions with its core…
HomE: Homography-Equivariant Video Representation Learning
Anirudh Sriram, Adrien Gaidon, Jiajun Wu +3
Recent advances in self-supervised representation learning have enabled more efficient and robust model performance without relying on extensive labeled data. However, most works a…
Procedure-Aware Pretraining for Instructional Video Understanding
Honglu Zhou, Roberto Martín-Martín, Mubbasir Kapadia +2
Our goal is to learn a video representation that is useful for downstream procedure understanding tasks in instructional videos. Due to the small amount of available annotations, a…