137 citations · 339 across the 17 of their papers we have counts for
17 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…
Iterative Belief Propagation for Sparse Combinatorial Optimization
Sam Reifenstein, Timothée Leleu
In this note we study an iterative belief propagation (IBP) algorithm and demonstrate it's ability to solve sparse combinatorial optimization problems. Similar to simulated anneali…
Non-Equilibrium Dynamics of Hybrid Continuous-Discrete Ground-State Sampling
Timothée Leleu, Samuel Reifenstein
We propose a general framework for a hybrid continuous-discrete algorithm that integrates continuous-time deterministic dynamics with Metropolis-Hastings steps to combine search dy…
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…
Experimental observation of chimera states in spiking neural networks based on degenerate optical parametric oscillators
Tumi Makinwa, Kensuke Inaba, Takahiro Inagaki +9
We experimentally demonstrate that networks of identical photonic spiking neurons based on coupled degenerate parametric oscillators can show various chimera states, in which, depe…
Coherent Ising Machines with Optical Error Correction Circuits
Sam Reifenstein, Satoshi Kako, Farad Khoyratee +2
We propose a network of open-dissipative quantum oscillators with optical error correction circuits. In the proposed network, the squeezed/anti-squeezed vacuum states of the consti…
Collective and synchronous dynamics of photonic spiking neurons
Takahiro Inagaki, Kensuke Inaba, Timothée Leleu +7
Nonlinear dynamics of spiking neural networks has recently attracted much interest as an approach to understand possible information processing in the brain and apply it to artific…
Scaling advantage of nonrelaxational dynamics for high-performance combinatorial optimization
Timothee Leleu, Farad Khoyratee, Timothee Levi +3
The development of physical simulators, called Ising machines, that sample from low energy states of the Ising Hamiltonian has the potential to drastically transform our ability to…
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…
Coherent Ising machines -- Quantum optics and neural network perspectives
Y. Yamamoto, T. Leleu, S. Ganguli +1
A coherent Ising machine (CIM) is a network of optical parametric oscillators (OPOs), in which the "strongest" collective mode of oscillation at well above threshold corresponds to…
Coherent Ising machines with error correction feedback
Satoshi Kako, Timothée Leleu, Yoshitaka Inui +3
A non-equilibrium open-dissipative neural network, such as a coherent Ising machine based on mutually coupled optical parametric oscillators, has been proposed and demonstrated as…
Destabilization of local minima in analog spin systems by correction of amplitude heterogeneity
Timothee Leleu, Yoshihisa Yamamoto, Peter L. McMahon +1
The relaxation of binary spins to analog values has been the subject of much debate in the field of statistical physics, neural networks, and more recently quantum computing, notab…