36 citations · 53 across the 10 of their papers we have counts for
10 papers
Provable Length Generalization in Sequence Prediction via Spectral Filtering
Annie Marsden, Evan Dogariu, Naman Agarwal +3
We consider the problem of length generalization in sequence prediction. We define a new metric of performance in this setting -- the Asymmetric-Regret -- which measures regret aga…
FutureFill: Fast Generation from Convolutional Sequence Models
Naman Agarwal, Xinyi Chen, Evan Dogariu +6
We address the challenge of efficient auto-regressive generation in sequence prediction models by introducing FutureFill, a general-purpose fast generation method for any sequence…
Spectral State Space Models
Naman Agarwal, Daniel Suo, Xinyi Chen +1
This paper studies sequence modeling for prediction tasks with long range dependencies. We propose a new formulation for state space models (SSMs) based on learning linear dynamica…
Online Learning for Obstacle Avoidance
David Snyder, Meghan Booker, Nathaniel Simon +4
We approach the fundamental problem of obstacle avoidance for robotic systems via the lens of online learning. In contrast to prior work that either assumes worst-case realizations…
Benchmarking Neural Network Training Algorithms
George E. Dahl, Frank Schneider, Zachary Nado +22
Training algorithms, broadly construed, are an essential part of every deep learning pipeline. Training algorithm improvements that speed up training across a wide variety of workl…
Adaptive Gradient Methods at the Edge of Stability
Jeremy M. Cohen, Behrooz Ghorbani, Shankar Krishnan +8
Very little is known about the training dynamics of adaptive gradient methods like Adam in deep learning. In this paper, we shed light on the behavior of these algorithms in the fu…