1 citations · 1 across the 3 of their papers we have counts for
4 papers
End-to-End Test-Time Training for Long Context
Arnuv Tandon, Karan Dalal, Xinhao Li +11
We formulate long-context language modeling as a problem in continual learning rather than architecture design. Under this formulation, we only use a standard architecture -- a Tra…
Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures
Tyler A. Chang, Catherine Arnett, Abdelrahman Sadallah +377
To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we pre…
Beyond Scores: Proximal Diffusion Models
Zhenghan Fang, Mateo Díaz, Sam Buchanan +1
Diffusion models have quickly become some of the most popular and powerful generative models for high-dimensional data. The key insight that enabled their development was the reali…
What's in a Prior? Learned Proximal Networks for Inverse Problems
Zhenghan Fang, Sam Buchanan, Jeremias Sulam
Proximal operators are ubiquitous in inverse problems, commonly appearing as part of algorithmic strategies to regularize problems that are otherwise ill-posed. Modern deep learnin…