7 citations · 13 across the 4 of their papers we have counts for
4 papers
Tune As You Scale: Hyperparameter Optimization For Compute Efficient Training
Abraham J. Fetterman, Ellie Kitanidis, Joshua Albrecht +6
Hyperparameter tuning of deep learning models can lead to order-of-magnitude performance gains for the same amount of compute. Despite this, systematic tuning is uncommon, particul…
On the Stepwise Nature of Self-Supervised Learning
James B. Simon, Maksis Knutins, Liu Ziyin +3
We present a simple picture of the training process of joint embedding self-supervised learning methods. We find that these methods learn their high-dimensional embeddings one dime…
Despite "super-human" performance, current LLMs are unsuited for decisions about ethics and safety
Joshua Albrecht, Ellie Kitanidis, Abraham J. Fetterman
Large language models (LLMs) have exploded in popularity in the past few years and have achieved undeniably impressive results on benchmarks as varied as question answering and tex…
Avalon: A Benchmark for RL Generalization Using Procedurally Generated Worlds
Joshua Albrecht, Abraham J. Fetterman, Bryden Fogelman +8
Despite impressive successes, deep reinforcement learning (RL) systems still fall short of human performance on generalization to new tasks and environments that differ from their…