8 papers
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…
Hybrid Quantum-Classical Optimization for Multi-Objective Supply Chain Logistics
Raoul Heese, Timothée Leleu, Sam Reifenstein +2
A multi-objective logistics optimization problem from a real-world supply chain is formulated as a Quadratic Unconstrained Binary Optimization Problem (QUBO) that minimizes cost, e…
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…
Reshaping Global Loop Structure to Accelerate Local Optimization by Smoothing Rugged Landscapes
Timothee Leleu, Sam Reifenstein, Atsushi Yamamura +1
Probabilistic graphical models with frustration exhibit rugged energy landscapes that trap iterative optimization dynamics. These landscapes are shaped not only by local interactio…
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…