43 citations
- Institut National des Sciences Appliquées de LyonFR2 papers
- New York UniversityUS2 papers
- Amazon (United States)US1 paper
- Centre de Recherche en Mathématiques de la DécisionFR1 paper
- Clarendon CollegeUS1 paper
- Eagle MountUS1 paper
- École Centrale de LyonFR1 paper
- École Normale Supérieure - PSLFR1 paper
- Georgia Institute of TechnologyUS1 paper
- Google (United States)US1 paper
- Institut national de recherche en sciences et technologies du numériqueFR1 paper
- Laboratoire de Physique de l'Ecole Normale SupérieureFR1 paper
9 papers
Mixed-Precision in adaptive Runge-Kutta method for large ODE systems
Mouhamad Al-Sayed, Samuel Bernard, Arsène Marzorati +1
Mixed-precision methods combine low and high precision arithmetics to exploit low precision computational speed and high precision accuracy. Large ODE systems that contain many het…
Investigating Modality Contribution in Audio LLMs for Music
Giovana Morais, Magdalena Fuentes
Audio Large Language Models (Audio LLMs) enable human-like conversation about music, yet it is unclear if they are truly listening to the audio or just using textual reasoning, as…
Velocity trapping in the lifted TASEP and the true self-avoiding random walk
Brune Massoulié, Clément Erignoux, Cristina Toninelli +1
We discuss non-reversible Markov-chain Monte Carlo algorithms that, for particle systems, rigorously sample the positional Boltzmann distribution and that have faster than physical…
Distilling Text Style Transfer With Self-Explanation From LLMs
Chiyu Zhang, Honglong Cai, Yuezhang +4
Text Style Transfer (TST) seeks to alter the style of text while retaining its core content. Given the constraints of limited parallel datasets for TST, we propose CoTeX, a framewo…
An Interdisciplinary Review of Music Performance Analysis
Alexander Lerch, Claire Arthur, Ashis Pati +1
A musical performance renders an acoustic realization of a musical score or other representation of a composition. Different performances of the same composition may vary in terms…
Multi-Task Self-Supervised Pre-Training for Music Classification
Ho-Hsiang Wu, Chieh-Chi Kao, Qingming Tang +4
Deep learning is very data hungry, and supervised learning especially requires massive labeled data to work well. Machine listening research often suffers from limited labeled data…