35 citations · 97 across the 9 of their papers we have counts for
9 papers
HPO: We won't get fooled again
Kalifou René Traoré, Andrés Camero, Xiao Xiang Zhu
Hyperparameter optimization (HPO) is a well-studied research field. However, the effects and interactions of the components in an HPO pipeline are not yet well investigated. Then,…
Landscape of Neural Architecture Search across sensors: how much do they differ ?
Kalifou René Traoré, Andrés Camero, Xiao Xiang Zhu
With the rapid rise of neural architecture search, the ability to understand its complexity from the perspective of a search algorithm is desirable. Recently, Traoré et al. have pr…
A Data-driven Approach to Neural Architecture Search Initialization
Kalifou René Traoré, Andrés Camero, Xiao Xiang Zhu
Algorithmic design in neural architecture search (NAS) has received a lot of attention, aiming to improve performance and reduce computational cost. Despite the great advances made…
Fitness Landscape Footprint: A Framework to Compare Neural Architecture Search Problems
Kalifou René Traoré, Andrés Camero, Xiao Xiang Zhu
Neural architecture search is a promising area of research dedicated to automating the design of neural network models. This field is rapidly growing, with a surge of methodologies…
Lessons from the Clustering Analysis of a Search Space: A Centroid-based Approach to Initializing NAS
Kalifou Rene Traore, Andrés Camero, Xiao Xiang Zhu
Lots of effort in neural architecture search (NAS) research has been dedicated to algorithmic development, aiming at designing more efficient and less costly methods. Nonetheless,…
DisCoRL: Continual Reinforcement Learning via Policy Distillation
René Traoré, Hugo Caselles-Dupré, Timothée Lesort +4
In multi-task reinforcement learning there are two main challenges: at training time, the ability to learn different policies with a single model; at test time, inferring which of…
Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer
René Traoré, Hugo Caselles-Dupré, Timothée Lesort +3
We focus on the problem of teaching a robot to solve tasks presented sequentially, i.e., in a continual learning scenario. The robot should be able to solve all tasks it has encoun…
Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics
Antonin Raffin, Ashley Hill, René Traoré +3
Scaling end-to-end reinforcement learning to control real robots from vision presents a series of challenges, in particular in terms of sample efficiency. Against end-to-end learni…
S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning
Antonin Raffin, Ashley Hill, René Traoré +3
State representation learning aims at learning compact representations from raw observations in robotics and control applications. Approaches used for this objective are auto-encod…