output
20122025
most citedWasserstein Patch Prior for Image Superresolution

19 citations

14 papers

cs.LG2025

A Continual Offline Reinforcement Learning Benchmark for Navigation Tasks

Anthony Kobanda, Odalric-Ambrym Maillard, Rémy Portelas

Autonomous agents operating in domains such as robotics or video game simulations must adapt to changing tasks without forgetting about the previous ones. This process called Conti…

cs.LG2024

Hierarchical Subspaces of Policies for Continual Offline Reinforcement Learning

Anthony Kobanda, Rémy Portelas, Odalric-Ambrym Maillard +1

We consider a Continual Reinforcement Learning setup, where a learning agent must continuously adapt to new tasks while retaining previously acquired skill sets, with a focus on th…

cs.CL2024★ 7 cited

Stick to your Role! Stability of Personal Values Expressed in Large Language Models

Grgur Kovač, Rémy Portelas, Masataka Sawayama +2

The standard way to study Large Language Models (LLMs) with benchmarks or psychology questionnaires is to provide many different queries from similar minimal contexts (e.g. multipl…

cs.CV2023

Real-Time Neural Materials using Block-Compressed Features

Clément Weinreich, Louis de Oliveira, Antoine Houdard +1

Neural materials typically consist of a collection of neural features along with a decoder network. The main challenge in integrating such models in real-time rendering pipelines l…

cs.CL2023★ 10 cited

Towards Detecting Contextual Real-Time Toxicity for In-Game Chat

Zachary Yang, Nicolas Grenan-Godbout, Reihaneh Rabbany

Real-time toxicity detection in online environments poses a significant challenge, due to the increasing prevalence of social media and gaming platforms. We introduce ToxBuster, a…

cs.CL2023★ 9 cited

Large Language Models as Superpositions of Cultural Perspectives

Grgur Kovač, Masataka Sawayama, Rémy Portelas +3

Large Language Models (LLMs) are often misleadingly recognized as having a personality or a set of values. We argue that an LLM can be seen as a superposition of perspectives with…