425 citations · 868 across the 13 of their papers we have counts for
17 papers
Optimizing with Low Budgets: a Comparison on the Black-box Optimization Benchmarking Suite and OpenAI Gym
Elena Raponi, Nathanael Rakotonirina Carraz, Jérémy Rapin +2
The growing ubiquity of machine learning (ML) has led it to enter various areas of computer science, including black-box optimization (BBO). Recent research is particularly concern…
Code Llama: Open Foundation Models for Code
Baptiste Rozière, Jonas Gehring, Fabian Gloeckle +23
We release Code Llama, a family of large language models for code based on Llama 2 providing state-of-the-art performance among open models, infilling capabilities, support for lar…
Does Zero-Shot Reinforcement Learning Exist?
Ahmed Touati, Jérémy Rapin, Yann Ollivier
A zero-shot RL agent is an agent that can solve any RL task in a given environment, instantly with no additional planning or learning, after an initial reward-free learning phase.…
Decoding speech perception from non-invasive brain recordings
Alexandre Défossez, Charlotte Caucheteux, Jérémy Rapin +2
Decoding speech from brain activity is a long-awaited goal in both healthcare and neuroscience. Invasive devices have recently led to major milestones in that regard: deep learning…
Population Based Training for Data Augmentation and Regularization in Speech Recognition
Daniel Haziza, Jérémy Rapin, Gabriel Synnaeve
Varying data augmentation policies and regularization over the course of optimization has led to performance improvements over using fixed values. We show that population based tra…
Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking
Laurent Meunier, Herilalaina Rakotoarison, Pak Kan Wong +5
Existing studies in black-box optimization for machine learning suffer from low generalizability, caused by a typically selective choice of problem instances used for training and…