4 citations · 16 across the 9 of their papers we have counts for
5 papers · 1 filter
Does your data spark joy? Performance gains from domain upsampling at the end of training
Cody Blakeney, Mansheej Paul, Brett W. Larsen +2
Pretraining datasets for large language models (LLMs) have grown to trillions of tokens composed of large amounts of CommonCrawl (CC) web scrape along with smaller, domain-specific…
Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask?
Mansheej Paul, Feng Chen, Brett W. Larsen +3
Modern deep learning involves training costly, highly overparameterized networks, thus motivating the search for sparser networks that can still be trained to the same accuracy as…
Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks
Mansheej Paul, Brett W. Larsen, Surya Ganguli +2
A striking observation about iterative magnitude pruning (IMP; Frankle et al. 2020) is that $\unicode{x2014}$ after just a few hundred steps of dense training $\unicode{x2014}$ the…
How many degrees of freedom do we need to train deep networks: a loss landscape perspective
Brett W. Larsen, Stanislav Fort, Nic Becker +1
A variety of recent works, spanning pruning, lottery tickets, and training within random subspaces, have shown that deep neural networks can be trained using far fewer degrees of f…
Avoiding Spurious Local Minima in Deep Quadratic Networks
Abbas Kazemipour, Brett W. Larsen, Shaul Druckmann
Despite their practical success, a theoretical understanding of the loss landscape of neural networks has proven challenging due to the high-dimensional, non-convex, and highly non…