118 citations · 144 across the 6 of their papers we have counts for
6 papers
Replacing softmax with ReLU in Vision Transformers
Mitchell Wortsman, Jaehoon Lee, Justin Gilmer +1
Previous research observed accuracy degradation when replacing the attention softmax with a point-wise activation such as ReLU. In the context of vision transformers, we find that…
Small-scale proxies for large-scale Transformer training instabilities
Mitchell Wortsman, Peter J. Liu, Lechao Xiao +13
Teams that have trained large Transformer-based models have reported training instabilities at large scale that did not appear when training with the same hyperparameters at smalle…
Scaling Vision Transformers to 22 Billion Parameters
Mostafa Dehghani, Josip Djolonga, Basil Mustafa +39
The scaling of Transformers has driven breakthrough capabilities for language models. At present, the largest large language models (LLMs) contain upwards of 100B parameters. Visio…
Improving Training Stability for Multitask Ranking Models in Recommender Systems
Jiaxi Tang, Yoel Drori, Daryl Chang +6
Recommender systems play an important role in many content platforms. While most recommendation research is dedicated to designing better models to improve user experience, we foun…
Pre-training helps Bayesian optimization too
Zi Wang, George E. Dahl, Kevin Swersky +6
Bayesian optimization (BO) has become a popular strategy for global optimization of many expensive real-world functions. Contrary to a common belief that BO is suited to optimizing…
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach
Setareh Ariafar, Justin Gilmer, Zachary Nado +3
Black box optimization requires specifying a search space to explore for solutions, e.g. a d-dimensional compact space, and this choice is critical for getting the best results at…