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2020 – today
- 2025
[i20]Stephen Whitelam:
Generative thermodynamic computing. CoRR abs/2506.15121 (2025)- [i19]Andrew Ferguson, Marisa Lafleur, Lars Ruthotto, Jesse Thaler, Yuan-Sen Ting, Pratyush Tiwary, Soledad Villar, E. Paulo Alves, Jeremy Avigad, Simon Billinge, Camille L. Bilodeau, Keith Brown, Emmanuel J. Candès, Arghya Chattopadhyay, Bingqing Cheng, Jonathan Clausen, Connor W. Coley, Andrew J. Connolly, Fred Daum, Sijia Dong
, Chrisy Xiyu Du, Cora Dvorkin, Cristiano Fanelli, Eric B. Ford, Luis Manuel Frutos, Nicolás García Trillos, Cecilia Garraffo, Robert Ghrist, Rafael Gómez-Bombarelli, Gianluca Guadagni, Sreelekha Guggilam, Sergei Gukov, Juan B. Gutierrez, Salman Habib, Johannes Hachmann, Boris Hanin, Philip C. Harris, Murray Holland, Elizabeth Holm, Hsin-Yuan Huang, Shih-Chieh Hsu, Nick Jackson, Olexandr Isayev, Heng Ji, Aggelos K. Katsaggelos, Jeremy Kepner, Yannis G. Kevrekidis, Michelle P. Kuchera, J. Nathan Kutz, Branislava Lalic, Ann Lee, Matt LeBlanc, Josiah Lim, Rebecca Lindsey, Yongmin Liu, Peter Y. Lu, Sudhir Malik, Vuk Mandic, Vidya B. Manian, Emeka P. Mazi, Pankaj Mehta, Peter Melchior, Brice Ménard, Jennifer Ngadiuba, Stella Offner, Elsa Olivetti, Shyue Ping Ong, Christopher Rackauckas, Philippe Rigollet, Chad Risko, Philip Romero, Grant M. Rotskoff, Brett Savoie, Uros Seljak, David Shih, Gary Shiu, Dima Shlyakhtenko, Eva Silverstein, Taylor Sparks, Thomas Strohmer, Christopher Stubbs, Stephen Thomas, Suriyanarayanan Vaikuntanathan, René Vidal, Francisco Villaescusa-Navarro, Gregory Voth, Benjamin Wandelt, Rachel Ward, Melanie Weber, Risa Wechsler, Stephen Whitelam, Olaf Wiest, Mike Williams, Zhuoran Yang, Yaroslava G. Yingling, Bin Yu, Shuwen Yue, Ann Zabludoff, Huimin Zhao, Tong Zhang:
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS). CoRR abs/2509.02661 (2025) - [i18]Stephen Whitelam:
Training thermodynamic computers by gradient descent. CoRR abs/2509.15324 (2025) - 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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