68 citations · 160 across the 28 of their papers we have counts for
4 papers · 1 filter
Width and Depth Limits Commute During Training in Linear Residual Networks
Ruijia Zhang, Soufiane Hayou
Recent work established that the infinite-width and infinite-depth limits of residual networks commute at initialization when residual branches are scaled by . In p…
Training-Free versus Training-Based Intent Classification in LLMs: Accuracy, Robustness, and Failure Modes
Nan Chen, Zhouhao Yang, Soufiane Hayou
Intent classification in Large Language Models (LLMs) involves categorizing user prompts into predefined classes. For instance, given a user prompt, the system must determine wheth…
The Myth of Expert Specialization in MoEs: Why Routing Reflects Geometry, Not Necessarily Domain Expertise
Xi Wang, Soufiane Hayou, Eric Nalisnick
Mixture of Experts (MoEs) are now ubiquitous in large language models, yet the mechanisms behind their "expert specialization" remain poorly understood. We show that, since MoE rou…
Learning Rate Scaling across LoRA Ranks and Transfer to Full Finetuning
Nan Chen, Soledad Villar, Soufiane Hayou
Low-Rank Adaptation (LoRA) is a standard tool for parameter-efficient finetuning of large models. While it induces a small memory footprint, its training dynamics can be surprising…