Stephen Whitelam

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 17

Top Topics

Stochastic Sampling

Reinforcement Learning

Markovian Decision

Large Deviations

Top Venues

Mach. Learn. Sci. Technol.

J. Comput. Chem.

Publications

Stephen Whitelam
Springs and a stopwatch: neural units with time-dependent multifunctionality. CoRR (2024)
Stephen Whitelam
How to train your demon to do fast information erasure without heat production. CoRR (2023)
Stephen Whitelam, Viktor Selin, Ian Benlolo, Corneel Casert, Isaac Tamblyn
Training neural networks using Metropolis Monte Carlo and an adaptive variant. Mach. Learn. Sci. Technol. 3 (4) (2022)
Stephen Whitelam, Viktor Selin, Ian Benlolo, Isaac Tamblyn
Training neural networks using Metropolis Monte Carlo and an adaptive variant. CoRR (2022)
Stephen Whitelam
Demon in the machine: learning to extract work and absorb entropy from fluctuating nanosystems. CoRR (2022)
Corneel Casert, Isaac Tamblyn, Stephen Whitelam
Learning stochastic dynamics and predicting emergent behavior using transformers. CoRR (2022)
Stephen Whitelam, Isaac Tamblyn
Cellular automata can classify data by inducing trajectory phase coexistence. CoRR (2022)
Stephen Whitelam
Improving the Accuracy of Nearest-Neighbor Classification Using Principled Construction and Stochastic Sampling of Training-Set Centroids. Entropy 23 (2) (2021)
Kyle Sprague, Juan Carrasquilla, Stephen Whitelam, Isaac Tamblyn
Watch and learn - a generalized approach for transferrable learning in deep neural networks via physical principles. Mach. Learn. Sci. Technol. 2 (2) (2021)
Corneel Casert, Tom Vieijra, Stephen Whitelam, Isaac Tamblyn
Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz. CoRR (2020)
Stephen Whitelam, Viktor Selin, Sang-Won Park, Isaac Tamblyn
Correspondence between neuroevolution and gradient descent. CoRR (2020)
Kyle Sprague, Juan Carrasquilla, Stephen Whitelam, Isaac Tamblyn
Watch and learn - a generalized approach for transferrable learning in deep neural networks via physical principles. CoRR (2020)
Stephen Whitelam, Isaac Tamblyn
Neuroevolutionary learning of particles and protocols for self-assembly. CoRR (2020)
Stephen Whitelam, Isaac Tamblyn
Learning to grow: control of materials self-assembly using evolutionary reinforcement learning. CoRR (2019)
Stephen Whitelam, Daniel Jacobson, Isaac Tamblyn
Evolutionary reinforcement learning of dynamical large deviations. CoRR (2019)
Chris Beeler, Uladzimir Yahorau, Rory Coles, Kyle Mills, Stephen Whitelam, Isaac Tamblyn
Optimizing thermodynamic trajectories using evolutionary reinforcement learning. CoRR (2019)
Stephen Whitelam
Simple coarse graining and sampling strategies for image recognition. CoRR (2018)
Dina T. Mirijanian, Ranjan V. Mannige, Ronald N. Zuckermann, Stephen Whitelam
Development and use of an atomistic CHARMM-based forcefield for peptoid simulation. J. Comput. Chem. 35 (5) (2014)