Grant M. Rotskoff
Grant M. Rotskoff
Department of Chemistry, Stanford University
Verified email at - Homepage
Cited by
Cited by
Trainability and accuracy of artificial neural networks: An interacting particle system approach
G Rotskoff, E Vanden‐Eijnden
Communications on Pure and Applied Mathematics 75 (9), 1889-1935, 2022
Single-particle mapping of nonequilibrium nanocrystal transformations
X Ye, MR Jones, LB Frechette, Q Chen, AS Powers, P Ercius, G Dunn, ...
Science 354 (6314), 874-877, 2016
Inferring dissipation from current fluctuations
TR Gingrich, GM Rotskoff, JM Horowitz
Journal of Physics A: Mathematical and Theoretical 50 (18), 184004, 2017
Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks
G Rotskoff, E Vanden-Eijnden
Advances in neural information processing systems 31, 2018
Adaptive Monte Carlo augmented with normalizing flows
M Gabriť, GM Rotskoff, E Vanden-Eijnden
Proceedings of the National Academy of Sciences 119 (10), e2109420119, 2022
Transition-tempered metadynamics: Robust, convergent metadynamics via on-the-fly transition barrier estimation
JF Dama, G Rotskoff, M Parrinello, GA Voth
Journal of Chemical Theory and Computation 10 (9), 3626-3633, 2014
Geometric approach to optimal nonequilibrium control: Minimizing dissipation in nanomagnetic spin systems
GM Rotskoff, GE Crooks, E Vanden-Eijnden
Physical Review E 95 (1), 012148, 2017
Optimal control in nonequilibrium systems: Dynamic Riemannian geometry of the Ising model
GM Rotskoff, GE Crooks
Physical Review E 92 (6), 060102, 2015
Unraveling kinetically-driven mechanisms of gold nanocrystal shape transformations using graphene liquid cell electron microscopy
MR Hauwiller, LB Frechette, MR Jones, JC Ondry, GM Rotskoff, ...
Nano letters 18 (9), 5731-5737, 2018
Efficiency and large deviations in time-asymmetric stochastic heat engines
TR Gingrich, GM Rotskoff, S Vaikuntanathan, PL Geissler
New Journal of Physics 16 (10), 102003, 2014
Neuron birth-death dynamics accelerates gradient descent and converges asymptotically
G Rotskoff, S Jelassi, J Bruna, E Vanden-Eijnden
International Conference on Machine Learning, 2019
Near-optimal protocols in complex nonequilibrium transformations
TR Gingrich, GM Rotskoff, GE Crooks, PL Geissler
Proceedings of the National Academy of Sciences 113 (37), 10263-10268, 2016
Structural basis of a protein partner switch that regulates the general stress response of α-proteobacteria
J Herrou, G Rotskoff, Y Luo, B Roux, S Crosson
Proceedings of the National Academy of Sciences 109 (21), E1415-E1423, 2012
Structural asymmetry in a conserved signaling system that regulates division, replication, and virulence of an intracellular pathogen
JW Willett, J Herrou, A Briegel, G Rotskoff, S Crosson
Proceedings of the National Academy of Sciences 112 (28), E3709-E3718, 2015
Robust nonequilibrium pathways to microcompartment assembly
GM Rotskoff, PL Geissler
Proceedings of the National Academy of Sciences 115 (25), 6341-6346, 2018
Molecular simulation workflows as parallel algorithms: the execution engine of Copernicus, a distributed high-performance computing platform
S Pronk, I Pouya, M Lundborg, G Rotskoff, B Wesen, PM Kasson, ...
Journal of chemical theory and computation 11 (6), 2600-2608, 2015
A mean-field analysis of two-player zero-sum games
C Domingo-Enrich, S Jelassi, A Mensch, G Rotskoff, J Bruna
NeurIPS 2020, 2020
Necessity of capillary modes in a minimal model of nanoscale hydrophobic solvation
S Vaikuntanathan, G Rotskoff, A Hudson, PL Geissler
Proceedings of the National Academy of Sciences 113 (16), E2224-E2230, 2016
A dynamical central limit theorem for shallow neural networks
Z Chen, G Rotskoff, J Bruna, E Vanden-Eijnden
Advances in Neural Information Processing Systems 33, 22217-22230, 2020
Active importance sampling for variational objectives dominated by rare events: Consequences for optimization and generalization
GM Rotskoff, AR Mitchell, E Vanden-Eijnden
Mathematical and Scientific Machine Learning, 757-780, 2022
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