4 papers
Extracting Algorithms in Pre-trained LLMs: A Case on Hidden Markov Models
Yijia Dai, Zhaolin Gao, Yahya Sattar +2
Large language models (LLMs) display a striking ability to predict next observations from Hidden Markov Models (HMMs) via in-context learning (ICL), but the algorithm underlying th…
Pre-trained Large Language Models Learn Hidden Markov Models In-context
Yijia Dai, Zhaolin Gao, Yahya Sattar +2
Hidden Markov Models (HMMs) are foundational tools for modeling sequential data with latent Markovian structure, yet fitting them to real-world data remains computationally challen…
How many classes do we need to see for novel class discovery?
Akanksha Sarkar, Been Kim, Jennifer J. Sun
Novel class discovery is essential for ML models to adapt to evolving real-world data, with applications ranging from scientific discovery to robotics. However, these datasets cont…
Learning Keypoints for Multi-Agent Behavior Analysis using Self-Supervision
Daniel Khalil, Christina Liu, Pietro Perona +2
The study of social interactions and collective behaviors through multi-agent video analysis is crucial in biology. While self-supervised keypoint discovery has emerged as a promis…