10 citations · 20 across the 7 of their papers we have counts for
4 papers · 1 filter
Probabilistic Algorithms for Ising Machines from Optimization to Generative AI
Corentin Delacour, Xiuqi Zhang, Abdelrahman S. Abdelrahman +4
Ising machines have emerged as promising hardware accelerators for intractable optimization and sampling problems, yet their practical impact increasingly hinges on the co-design o…
Stochastic Sparse Attention for Memory-Bound Inference
Kyle Lee, Corentin Delacour, Kevin Callahan-Coray +5
Autoregressive decoding becomes bandwidth-limited at long contexts, as generating each token requires reading all key and value vectors from KV cache. We present Stochastic A…
IsingFormer: Augmenting Parallel Tempering With Learned Proposals
Saleh Bunaiyan, Corentin Delacour, Shuvro Chowdhury +3
Generative models have been extensively used to accelerate MCMC mixing for sampling and optimization, but their effective integration with standard MCMC remains an open question. H…
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…