All papers authored by Grant M. Rotskoff
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Carlos L. Bassani 
,
Greg van Anders ,
Uri Banin ,
Dmitry Baranov ,
Qian Chen ,
Marjolein Dijkstra ,
Michael S. Dimitriyev ,
Efi Efrati,
Jordi Faraudo ,
Oleg Gang ,
Nicola Gaston ,
Ramin Golestanian ,
G. Ivan Guerrero-Garcia ,
Michael Gruenwald ,
Amir Haji-Akbari
and 27 other authors
,
Greg van Anders ,
Uri Banin ,
Dmitry Baranov ,
Qian Chen ,
Marjolein Dijkstra ,
Michael S. Dimitriyev ,
Efi Efrati,
Jordi Faraudo ,
Oleg Gang ,
Nicola Gaston ,
Ramin Golestanian ,
G. Ivan Guerrero-Garcia ,
Michael Gruenwald ,
Amir Haji-Akbari
and 27 other authors
Nanocrystal Assemblies: Current Advances and Open Problems
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Yinuo Ren,
Yiping Lu,
Lexing Ying,
Grant M. Rotskoff ,
M. Wooldridge,
J. Dy,
S. Natarajan
,
M. Wooldridge,
J. Dy,
S. Natarajan
Statistical Spatially Inhomogeneous Diffusion Inference
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2023
Shriram Chennakesavalu ,
David J. Toomer ,
Grant M. Rotskoff
,
David J. Toomer ,
Grant M. Rotskoff
Ensuring thermodynamic consistency with invertible coarse-graining
Download from aip.scitation.org
Gregory R. Bowman,
Stephen J. Cox,
Christoph Dellago,
Kateri H. DuBay,
Joel D. Eaves,
Daniel A. Fletcher,
Layne B. Frechette,
Michael Grünwald,
Katherine Klymko,
JiYeon Ku,
Ahmad K. Omar,
Eran Rabani,
David R. Reichman,
Julia R. Rogers,
Andreana M. Rosnik
and 7 other authors
Remembering the Work of Phillip L. Geissler: A Coda to His Scientific Trajectory
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Grant Rotskoff ,
Eric Vanden‐Eijnden
,
Eric Vanden‐Eijnden
Trainability and Accuracy of Artificial Neural Networks: An Interacting Particle System Approach
Download from onlinelibrary.wiley.com
Marylou Gabrié ,
Grant M. Rotskoff ,
Eric Vanden-Eijnden
,
Grant M. Rotskoff ,
Eric Vanden-Eijnden
Adaptive Monte Carlo augmented with normalizing flows
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2021
Shriram Chennakesavalu ,
Grant M. Rotskoff
,
Grant M. Rotskoff
Probing the theoretical and computational limits of dissipative design
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2020
Zhengdao Chen,
Eric Vanden-Eijnden,
Joan Bruna,
H. Larochelle,
Grant M. Rotskoff ,
M. Ranzato,
R. Hadsell,
M. F. Balcan,
H. Lin
,
M. Ranzato,
R. Hadsell,
M. F. Balcan,
H. Lin
A Dynamical Central Limit Theorem for Shallow Neural Networks
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