no code implementations • 13 Jun 2023 • Abraham J. Fetterman, Ellie Kitanidis, Joshua Albrecht, Zachary Polizzi, Bryden Fogelman, Maksis Knutins, Bartosz Wróblewski, James B. Simon, Kanjun Qiu
Hyperparameter tuning of deep learning models can lead to order-of-magnitude performance gains for the same amount of compute.
1 code implementation • 27 Mar 2023 • James B. Simon, Maksis Knutins, Liu Ziyin, Daniel Geisz, Abraham J. Fetterman, Joshua Albrecht
We present a simple picture of the training process of joint embedding self-supervised learning methods.
no code implementations • 13 Dec 2022 • 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 text summarization.
1 code implementation • 24 Oct 2022 • Joshua Albrecht, Abraham J. Fetterman, Bryden Fogelman, Ellie Kitanidis, Bartosz Wróblewski, Nicole Seo, Michael Rosenthal, Maksis Knutins, Zachary Polizzi, James B. Simon, Kanjun Qiu
As a benchmark tailored for studying RL generalization, we introduce Avalon, a set of tasks in which embodied agents in highly diverse procedural 3D worlds must survive by navigating terrain, hunting or gathering food, and avoiding hazards.
no code implementations • 25 Sep 2019 • Abraham J. Fetterman, Christina H. Kim, Joshua Albrecht
Abstract Stochastic gradient descent (SGD) and Adam are commonly used to optimize deep neural networks, but choosing one usually means making tradeoffs between speed, accuracy and stability.