10 citations · 17 across the 11 of their papers we have counts for
6 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…
SANTA++: Sampling Attention through Representative Keys
Kyle Lee, Christian Z. Pratt, Ruoyu Fang +4
Attention often concentrates on a small subset of tokens in the context, but which subset matters changes from one query to the next. To exploit this changing structure, we introdu…
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…
From Independent to Correlated Diffusion: Generalized Generative Modeling with Probabilistic Computers
Nihal Sanjay Singh, Mazdak Mohseni-Rajaee, Shaila Niazi +1
Diffusion models have emerged as a powerful framework for generative tasks in deep learning. They decompose generative modeling into two computational primitives: deterministic neu…
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…