98 citations
- Laboratoire Interdisciplinaire des Sciences du NumériqueFR10 papers
- Université Paris-SaclayFR8 papers
- Centre National de la Recherche ScientifiqueFR6 papers
- Centre Inria de SaclayFR2 papers
- ChaLearn2 papers
- Computer Vision Center2 papers
- Électricité de France (France)FR2 papers
- Institut national de recherche en sciences et technologies du numériqueFR2 papers
- Laboratoire Interdisciplinaire de PhysiqueFR2 papers
- Universitat de BarcelonaES2 papers
- Université Grenoble AlpesFR2 papers
- Université Paris-SudFR2 papers
15 papers
Stylized Meta-Album: Group-bias injection with style transfer to study robustness against distribution shifts
Romain Mussard, Aurélien Gauffre, Ihsan Ullah +4
We introduce Stylized Meta-Album (SMA), a new image classification meta-dataset comprising 24 datasets (12 content datasets, and 12 stylized datasets), designed to advance studies…
Fair Universe Higgs Uncertainty Challenge
Ragansu Chakkappai, Wahid Bhimji, Paolo Calafiura +16
This competition in high-energy physics (HEP) and machine learning was the first to strongly emphasise uncertainties in cross-section measurement. Partici…
Usefulness of LLMs as an Author Checklist Assistant for Scientific Papers: NeurIPS'24 Experiment
Alexander Goldberg, Ihsan Ullah, Thanh Gia Hieu Khuong +4
Large language models (LLMs) represent a promising, but controversial, tool in aiding scientific peer review. This study evaluates the usefulness of LLMs in a conference setting as…
RelevAI-Reviewer: A Benchmark on AI Reviewers for Survey Paper Relevance
Paulo Henrique Couto, Quang Phuoc Ho, Nageeta Kumari +4
Recent advancements in Artificial Intelligence (AI), particularly the widespread adoption of Large Language Models (LLMs), have significantly enhanced text analysis capabilities. T…
RRR-Net: Reusing, Reducing, and Recycling a Deep Backbone Network
Haozhe Sun, Isabelle Guyon, Felix Mohr +1
It has become mainstream in computer vision and other machine learning domains to reuse backbone networks pre-trained on large datasets as preprocessors. Typically, the last layer…
Modularity in Deep Learning: A Survey
Haozhe Sun, Isabelle Guyon
Modularity is a general principle present in many fields. It offers attractive advantages, including, among others, ease of conceptualization, interpretability, scalability, module…