24 citations · 60 across the 10 of their papers we have counts for
4 papers · 1 filter
Inference for Probabilistic Dependency Graphs
Oliver E. Richardson, Joseph Y. Halpern, Christopher De Sa
Probabilistic dependency graphs (PDGs) are a flexible class of probabilistic graphical models, subsuming Bayesian Networks and Factor Graphs. They can also capture inconsistent bel…
QuIP: 2-Bit Quantization of Large Language Models With Guarantees
Jerry Chee, Yaohui Cai, Volodymyr Kuleshov +1
This work studies post-training parameter quantization in large language models (LLMs). We introduce quantization with incoherence processing (QuIP), a new method based on the insi…
InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models
Yingheng Wang, Yair Schiff, Aaron Gokaslan +4
While diffusion models excel at generating high-quality samples, their latent variables typically lack semantic meaning and are not suitable for representation learning. Here, we p…
TART: A plug-and-play Transformer module for task-agnostic reasoning
Kush Bhatia, Avanika Narayan, Christopher De Sa +1
Large language models (LLMs) exhibit in-context learning abilities which enable the same model to perform several tasks without any task-specific training. In contrast, traditional…