22k citations · 27.6k across the 41 of their papers we have counts for
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VeLO: Training Versatile Learned Optimizers by Scaling Up
Luke Metz, James Harrison, C. Daniel Freeman +8
While deep learning models have replaced hand-designed features across many domains, these models are still trained with hand-designed optimizers. In this work, we leverage the sam…
The Efficiency Misnomer
Mostafa Dehghani, Anurag Arnab, Lucas Beyer +2
Model efficiency is a critical aspect of developing and deploying machine learning models. Inference time and latency directly affect the user experience, and some applications hav…
MULEX: Disentangling Exploitation from Exploration in Deep RL
Lucas Beyer, Damien Vincent, Olivier Teboul +3
An agent learning through interactions should balance its action selection process between probing the environment to discover new rewards and using the information acquired in the…
Deep multi-class learning from label proportions
Gabriel Dulac-Arnold, Neil Zeghidour, Marco Cuturi +2
We propose a learning algorithm capable of learning from label proportions instead of direct data labels. In this scenario, our data are arranged into various bags of a certain siz…