3 papers
cs.AI2025
Efficient Prediction of Pass@k Scaling in Large Language Models
Joshua Kazdan, Rylan Schaeffer, Youssef Allouah +4
Assessing the capabilities and risks of frontier AI systems is a critical area of research, and recent work has shown that repeated sampling from models can dramatically increase b…
cs.LG2024
Decoupled Weight Decay for Any Norm
Nadav Joseph Outmezguine, Noam Levi
With the success of deep neural networks (NNs) in a variety of domains, the computational and storage requirements for training and deploying large NNs have become a bottleneck for…
cond-mat.stat-mech2023
Charting the Topography of the Neural Network Landscape with Thermal-Like Noise
Theo Jules, Gal Brener, Tal Kachman +2
The training of neural networks is a complex, high-dimensional, non-convex and noisy optimization problem whose theoretical understanding is interesting both from an applicative pe…