4.7k citations · 10.6k across the 33 of their papers we have counts for
23 papers · 1 filter
Has the Machine Learning Review Process Become More Arbitrary as the Field Has Grown? The NeurIPS 2021 Consistency Experiment
Alina Beygelzimer, Yann N. Dauphin, Percy Liang +1
We present the NeurIPS 2021 consistency experiment, a larger-scale variant of the 2014 NeurIPS experiment in which 10% of conference submissions were reviewed by two independent co…
JaxPruner: A concise library for sparsity research
Joo Hyung Lee, Wonpyo Park, Nicole Mitchell +22
This paper introduces JaxPruner, an open-source JAX-based pruning and sparse training library for machine learning research. JaxPruner aims to accelerate research on sparse neural…
SAM operates far from home: eigenvalue regularization as a dynamical phenomenon
Atish Agarwala, Yann N. Dauphin
The Sharpness Aware Minimization (SAM) optimization algorithm has been shown to control large eigenvalues of the loss Hessian and provide generalization benefits in a variety of se…
How do Authors' Perceptions of their Papers Compare with Co-authors' Perceptions and Peer-review Decisions?
Charvi Rastogi, Ivan Stelmakh, Alina Beygelzimer +7
How do author perceptions match up to the outcomes of the peer-review process and perceptions of others? In a top-tier computer science conference (NeurIPS 2021) with more than 23,…
No One Representation to Rule Them All: Overlapping Features of Training Methods
Raphael Gontijo-Lopes, Yann Dauphin, Ekin D. Cubuk
Despite being able to capture a range of features of the data, high accuracy models trained with supervision tend to make similar predictions. This seemingly implies that high-perf…
Auxiliary Task Update Decomposition: The Good, The Bad and The Neutral
Lucio M. Dery, Yann Dauphin, David Grangier
While deep learning has been very beneficial in data-rich settings, tasks with smaller training set often resort to pre-training or multitask learning to leverage data from other t…