4 papers
Scalable quantum simulation of continuous-time generative models via tensor networks
Nathan X. Kodama, L. Andrew Wray, Sam Cochran +3
Continuous-time flow and diffusion models are widely used across many application domains, from large-scale deployment in computer vision and protein folding to emerging adoption f…
Diffusion-Guided Renormalization of Neural Systems via Tensor Networks
Nathan X. Kodama
Far from equilibrium, neural systems self-organize across multiple scales. Exploiting multiscale self-organization in neuroscience and artificial intelligence requires a computatio…
Thermodynamic Performance Limits for Score-Based Diffusion Models
Nathan X. Kodama, Michael Hinczewski
We establish a fundamental connection between score-based diffusion models and non-equilibrium thermodynamics by deriving performance limits based on entropy rates. Our main theore…
Latent Graph Learning in Generative Models of Neural Signals
Nathan X. Kodama, Kenneth A. Loparo
Inferring temporal interaction graphs and higher-order structure from neural signals is a key problem in building generative models for systems neuroscience. Foundation models for…