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Learning from learning machines: a new generation of AI technology to
  meet the needs of science

Learning from learning machines: a new generation of AI technology to meet the needs of science

27 November 2021
L. Pion-Tonachini
K. Bouchard
Héctor García Martín
S. Peisert
W. B. Holtz
A. Aswani
D. Dwivedi
H. Wainwright
G. Pilania
Benjamin Nachman
B. Marrone
N. Falco
P. Prabhat
Daniel B. Arnold
Alejandro Wolf-Yadlin
Sarah Powers
S. Climer
Q. Jackson
Ty Carlson
M. Sohn
P. Zwart
Neeraj Kumar
Amy Justice
Claire Tomlin
Daniel A. Jacobson
G. Micklem
Georgios Gkoutos
Peter J. Bickel
J. Cazier
Juliane Müller
B. Webb-Robertson
Rick L. Stevens
Mark Anderson
Ken Kreutz-Delgado
Michael W. Mahoney
James B. Brown
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Papers citing "Learning from learning machines: a new generation of AI technology to meet the needs of science"

4 / 4 papers shown
Title
Learning continuous models for continuous physics
Learning continuous models for continuous physics
Aditi S. Krishnapriyan
A. Queiruga
N. Benjamin Erichson
Michael W. Mahoney
AI4CE
21
32
0
17 Feb 2022
Distribution-Free, Risk-Controlling Prediction Sets
Distribution-Free, Risk-Controlling Prediction Sets
Stephen Bates
Anastasios Nikolas Angelopoulos
Lihua Lei
Jitendra Malik
Michael I. Jordan
OOD
178
185
0
07 Jan 2021
Decoupling Representation Learning from Reinforcement Learning
Decoupling Representation Learning from Reinforcement Learning
Adam Stooke
Kimin Lee
Pieter Abbeel
Michael Laskin
SSL
DRL
282
339
0
14 Sep 2020
Methods for Interpreting and Understanding Deep Neural Networks
Methods for Interpreting and Understanding Deep Neural Networks
G. Montavon
Wojciech Samek
K. Müller
FaML
234
2,235
0
24 Jun 2017
1