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Bridging Machine Learning and Sciences: Opportunities and Challenges

Bridging Machine Learning and Sciences: Opportunities and Challenges

Taoli Cheng
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Papers citing "Bridging Machine Learning and Sciences: Opportunities and Challenges"

6 / 6 papers shown
Title
Open-Set Recognition: a Good Closed-Set Classifier is All You Need?
Open-Set Recognition: a Good Closed-Set Classifier is All You Need?
S. Vaze
Kai Han
Andrea Vedaldi
Andrew Zisserman
BDL
169
404
0
12 Oct 2021
On the Impact of Spurious Correlation for Out-of-distribution Detection
On the Impact of Spurious Correlation for Out-of-distribution Detection
Yifei Ming
Hang Yin
Yixuan Li
OODD
151
74
0
12 Sep 2021
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
M. Bronstein
Joan Bruna
Taco S. Cohen
Petar Velivcković
GNN
174
1,104
0
27 Apr 2021
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
Simon L. Batzner
Albert Musaelian
Lixin Sun
Mario Geiger
J. Mailoa
M. Kornbluth
N. Molinari
Tess E. Smidt
Boris Kozinsky
203
1,238
0
08 Jan 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
270
5,660
0
05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,136
0
06 Jun 2015