3 citations · 8 across the 8 of their papers we have counts for
9 papers
CN101 - A Digital Thermodynamic Computer for Generative AI
Lars Holdijk, Denis Melanson, Zier Mensch +14
Thermodynamic computing is an emerging hardware paradigm, in which stochastic physical dynamics serve as the direct computational primitive. The recent explosion of generative AI h…
Robust Stochastic Gradient Posterior Sampling with Lattice Based Discretisation
Zier Mensch, Lars Holdijk, Samuel Duffield +4
Stochastic-gradient MCMC methods enable scalable Bayesian posterior sampling but often suffer from sensitivity to minibatch size and gradient noise. To address this, we propose Sto…
Lattice Random Walk Discretisations of Stochastic Differential Equations
Samuel Duffield, Maxwell Aifer, Denis Melanson +2
We introduce a lattice random walk discretisation scheme for stochastic differential equations (SDEs) that samples binary or ternary increments at each step, suppressing complex dr…
Scalable Thermodynamic Second-order Optimization
Kaelan Donatella, Samuel Duffield, Denis Melanson +7
Many hardware proposals have aimed to accelerate inference in AI workloads. Less attention has been paid to hardware acceleration of training, despite the enormous societal impact…
Thermodynamic Bayesian Inference
Maxwell Aifer, Samuel Duffield, Kaelan Donatella +6
A fully Bayesian treatment of complicated predictive models (such as deep neural networks) would enable rigorous uncertainty quantification and the automation of higher-level tasks…
Scalable Bayesian Learning with posteriors
Samuel Duffield, Kaelan Donatella, Johnathan Chiu +2
Although theoretically compelling, Bayesian learning with modern machine learning models is computationally challenging since it requires approximating a high dimensional posterior…
Thermodynamic Natural Gradient Descent
Kaelan Donatella, Samuel Duffield, Maxwell Aifer +3
Second-order training methods have better convergence properties than gradient descent but are rarely used in practice for large-scale training due to their computational overhead.…
Exploiting Inductive Biases in Video Modeling through Neural CDEs
Johnathan Chiu, Samuel Duffield, Max Hunter-Gordon +3
We introduce a novel approach to video modeling that leverages controlled differential equations (CDEs) to address key challenges in video tasks, notably video interpolation and ma…
Thermodynamic Matrix Exponentials and Thermodynamic Parallelism
Samuel Duffield, Maxwell Aifer, Gavin Crooks +2
Thermodynamic computing exploits fluctuations and dissipation in physical systems to efficiently solve various mathematical problems. For example, it was recently shown that certai…