2 citations · 8 across the 17 of their papers we have counts for
4 papers · 1 filter
Optimal Self-Consistency for Efficient Reasoning with Large Language Models
Austin Feng, Marius Alonso, Ambroise Odonnat +2
Self-consistency (SC) is a widely used test-time inference technique for improving performance in chain-of-thought reasoning. It consists of generating multiple responses, or ``sam…
Provable Benefits of In-Tool Learning for Large Language Models
Sam Houliston, Ambroise Odonnat, Charles Arnal +1
Tool-augmented language models, equipped with retrieval, memory, or external APIs, are reshaping AI, yet their theoretical advantages remain underexplored. In this paper, we addres…
CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data
Shifeng Xie, Vasilii Feofanov, Ambroise Odonnat +7
Time series foundation models (TSFMs) have recently gained significant attention due to their strong zero-shot capabilities and widespread real-world applications. Such models typi…
Easing Optimization Paths: a Circuit Perspective
Ambroise Odonnat, Wassim Bouaziz, Vivien Cabannes
Gradient descent is the method of choice for training large artificial intelligence systems. As these systems become larger, a better understanding of the mechanisms behind gradien…