Associate Professor at Princeton
Machine Learning Researcher
@jasondeanlee.bsky.social!
We prove a neural scaling law in the SGD learning of extensive width two-layer neural networks.
arxiv.org/abs/2504.19983
🧵below (1/10)
Reposted by Jason Lee
@jasondeanlee.bsky.social!
We prove a neural scaling law in the SGD learning of extensive width two-layer neural networks.
arxiv.org/abs/2504.19983
🧵below (1/10)
Reposted by Robert West, Helen Tager‐Flusberg, Catherine Kyobutungi , and 22 more Robert West, Helen Tager‐Flusberg, Catherine Kyobutungi, James P. Collins, Ankur R. Desai, Christoph Scherber, Julian L. Simon, Sarah Mitchell, Kai Sassenberg, Dirk Messner, Thomas Hanitzsch, Melissa L. Finucane, Rachel M. Gisselquist, Sandra González‐Bailón, Sandrine Sorlin, Serge Jaumain, Vanessa Manceron, Emanuela Galasso, David Lefebvre, Jason Lee, Dominique Fauteux, Lisa Hülsmann, Patrick Saccone, Antoine Cabon, Erin K. Buchholtz
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Reposted by Jason Lee
-Physicist Fritz Houtermans
There's a lot of truth to this. log-log plots are often abused and can be very misleading
1/5
Settling the sampling complexity of RL: arxiv.org/abs/2307.13586
Optimal Muti-Distribution Learning (solved a COLT 2023 open problem): arxiv.org/abs/2312.05134
Anytime Acceleration of Gradient Descent (solved a COLT 2024 open problem): arxiv.org/abs/2411.17668
Reposted by Jason Lee
A node using B receives a benefit with respect to X, but there is a benefit to using the same tech as the majority of your neighbors.
Assume everyone uses X at time t=0. Will they switch to B?
Reposted by Jason Lee
Reposted by Jason Lee