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Stephen Whitelam
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2020 – today
- 2024
- [i17]Stephen Whitelam:
Springs and a stopwatch: neural units with time-dependent multifunctionality. CoRR abs/2404.15545 (2024) - [i16]Leonardo Sabattini, Annalisa Coriolano
, Corneel Casert, Stiven Forti, Edward S. Barnard, Fabio Beltram, Massimiliano Pontil, Stephen Whitelam, Camilla Coletti, Antonio Rossi:
Adaptive AI-Driven Material Synthesis: Towards Autonomous 2D Materials Growth. CoRR abs/2410.10885 (2024) - [i15]Stephen Whitelam:
Increasing the clock speed of a thermodynamic computer by adding noise. CoRR abs/2410.12211 (2024) - [i14]Stephen Whitelam, Corneel Casert:
Thermodynamic computing out of equilibrium. CoRR abs/2412.17183 (2024) - 2023
- [i13]Stephen Whitelam:
How to train your demon to do fast information erasure without heat production. CoRR abs/2305.10607 (2023) - 2022
- [j4]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): 45026 (2022) - [i12]Corneel Casert, Isaac Tamblyn, Stephen Whitelam:
Learning stochastic dynamics and predicting emergent behavior using transformers. CoRR abs/2202.08708 (2022) - [i11]Stephen Whitelam, Isaac Tamblyn:
Cellular automata can classify data by inducing trajectory phase coexistence. CoRR abs/2203.05551 (2022) - [i10]Stephen Whitelam, Viktor Selin, Ian Benlolo, Isaac Tamblyn:
Training neural networks using Metropolis Monte Carlo and an adaptive variant. CoRR abs/2205.07408 (2022) - [i9]Stephen Whitelam:
Demon in the machine: learning to extract work and absorb entropy from fluctuating nanosystems. CoRR abs/2211.10853 (2022) - 2021
- [j3]Stephen Whitelam
Improving the Accuracy of Nearest-Neighbor Classification Using Principled Construction and Stochastic Sampling of Training-Set Centroids. Entropy 23(2): 149 (2021) - [j2]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): 02 (2021) - 2020
- [i8]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 abs/2003.02647 (2020) - [i7]Stephen Whitelam, Viktor Selin, Sang-Won Park, Isaac Tamblyn:
Correspondence between neuroevolution and gradient descent. CoRR abs/2008.06643 (2020) - [i6]Corneel Casert, Tom Vieijra, Stephen Whitelam, Isaac Tamblyn:
Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz. CoRR abs/2011.08657 (2020) - [i5]Stephen Whitelam, Isaac Tamblyn:
Neuroevolutionary learning of particles and protocols for self-assembly. CoRR abs/2012.11832 (2020)
2010 – 2019
- 2019
- [i4]Chris Beeler, Uladzimir Yahorau, Rory Coles, Kyle Mills, Stephen Whitelam, Isaac Tamblyn:
Optimizing thermodynamic trajectories using evolutionary reinforcement learning. CoRR abs/1903.08543 (2019) - [i3]Stephen Whitelam, Daniel Jacobson, Isaac Tamblyn:
Evolutionary reinforcement learning of dynamical large deviations. CoRR abs/1909.00835 (2019) - [i2]Stephen Whitelam, Isaac Tamblyn:
Learning to grow: control of materials self-assembly using evolutionary reinforcement learning. CoRR abs/1912.08333 (2019) - 2018
- [i1]Stephen Whitelam:
Simple coarse graining and sampling strategies for image recognition. CoRR abs/1809.02599 (2018) - 2014
- [j1]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): 360-370 (2014)
Coauthor Index
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