6 citations · 6 across the 2 of their papers we have counts for
5 papers
IsingFormer: Augmenting Parallel Tempering With Learned Proposals
Saleh Bunaiyan, Corentin Delacour, Shuvro Chowdhury +2
Markov Chain Monte Carlo (MCMC) underlies both statistical physics and combinatorial optimization, but mixes slowly near critical points and in rough landscapes. Parallel Tempering…
Two-dimensional Parallel Tempering for Constrained Optimization
Corentin Delacour, M Mahmudul Hasan Sajeeb, Joao P. Hespanha +1
Sampling Boltzmann probability distributions plays a key role in machine learning and optimization, motivating the design of hardware accelerators such as Ising machines. While the…
Lagrange Oscillatory Neural Networks for Constraint Satisfaction and Optimization
Corentin Delacour, Bram Haverkort, Filip Sabo +2
Physics-inspired computing paradigms are receiving renewed attention to enhance efficiency in compute-intensive tasks such as artificial intelligence and optimization. Similar to H…
Scalable Connectivity for Ising Machines: Dense to Sparse
M Mahmudul Hasan Sajeeb, Navid Anjum Aadit, Shuvro Chowdhury +7
In recent years, hardware implementations of Ising machines have emerged as a viable alternative to quantum computing for solving hard optimization problems among other application…
Self-Adaptive Ising Machines for Constrained Optimization
Corentin Delacour
Ising machines (IM) are physics-inspired alternatives to von Neumann architectures for solving hard optimization tasks. By mapping binary variables to coupled Ising spins, IMs can…