137 citations · 339 across the 17 of their papers we have counts for
6 papers · 1 filter
LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks
Tyler King, Timothee Leleu
Deploying neural networks on unconventional hardware demands architectures that co-optimize task accuracy and platform-specific constraints such as energy cost, physical non-ideali…
Improving Generalization by Permutation Routing Across Model Copies
Shuhei Kashiwamura, Timothee Leleu
We introduce a use of the \(M\)-cover (or \(M\)-layer) transform for machine learning. The method replicates a model \(M\) times, but instead of coupling the copies through paramet…
Contrastive Concept-Tree Search for LLM-Assisted Algorithm Discovery
Timothee Leleu, Sudeera Gunathilaka, Federico Ghimenti +1
Large language Model (LLM)-assisted algorithm discovery is an iterative, black-box optimization process over programs to approximatively solve a target task, where an LLM proposes…
Neural Ising Machines via Unrolling and Zeroth-Order Training
Sam Reifenstein, Timothee Leleu
We propose a data-driven heuristic for NP-hard Ising and Max-Cut optimization that learns the update rule of an iterative dynamical system. The method learns a shared, node-wise up…
Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization
Sam Reifenstein, Timothee Leleu, Yoshihisa Yamamoto
We propose a novel algorithm that extends the methods of ball smoothing and Gaussian smoothing for noisy derivative-free optimization by accounting for the heterogeneous curvature…
Model-Size Reduction for Reservoir Computing by Concatenating Internal States Through Time
Yusuke Sakemi, Kai Morino, Timothée Leleu +1
Reservoir computing (RC) is a machine learning algorithm that can learn complex time series from data very rapidly based on the use of high-dimensional dynamical systems, such as r…