2 citations · 2 across the 4 of their papers we have counts for
5 papers
Behavioral Foundation Models for Quality Diversity
Nazim Bendib, Nicolas Perrin-Gilbert, Olivier Sigaud
Behavioral Foundation Models (BFMs) are an emerging paradigm in reinforcement learning, playing a role analogous to large language models in natural language processing: they have…
Improving Zero-Shot Offline RL via Behavioral Task Sampling
Nazim Bendib, Nicolas Perrin-Gilbert, Olivier Sigaud
Offline zero-shot reinforcement learning (RL) aims to learn agents that optimize unseen reward functions without additional environment interaction. The standard approach to this p…
A Reinforcement Learning Environment for Automatic Code Optimization in the MLIR Compiler
Mohammed Tirichine, Nassim Ameur, Nazim Bendib +4
Code optimization is a crucial task that aims to enhance code performance. However, this process is often tedious and complex, highlighting the necessity for automatic code optimiz…
CoViews: Adaptive Augmentation Using Cooperative Views for Enhanced Contrastive Learning
Nazim Bendib
Data augmentation plays a critical role in generating high-quality positive and negative pairs necessary for effective contrastive learning. However, common practices involve using…
Supervised Contrastive Learning and Feature Fusion for Improved Kinship Verification
Nazim Bendib
Facial Kinship Verification is the task of determining the degree of familial relationship between two facial images. It has recently gained a lot of interest in various applicatio…