3 papers
cs.LG2026
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…
cs.ET2024
Mean-Field Assisted Deep Boltzmann Learning with Probabilistic Computers
Shuvro Chowdhury, Shaila Niazi, Kerem Y. Camsari
Despite their appeal as physics-inspired, energy-based and generative nature, general Boltzmann Machines (BM) are considered intractable to train. This belief led to simplified mod…
cond-mat.mes-hall2023
CMOS + stochastic nanomagnets: heterogeneous computers for probabilistic inference and learning
Nihal Sanjay Singh, Keito Kobayashi, Qixuan Cao +8
Extending Moore's law by augmenting complementary-metal-oxide semiconductor (CMOS) transistors with emerging nanotechnologies (X) has become increasingly important. One important c…