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Beyond Point Estimate: Inferring Ensemble Prediction Variation from
  Neuron Activation Strength in Recommender Systems

Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems

17 August 2020
Zhe Chen
Yuyan Wang
Dong Lin
D. Cheng
Lichan Hong
Ed H. Chi
Claire Cui
ArXivPDFHTML

Papers citing "Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems"

25 / 25 papers shown
Title
Diverse Ensembles Improve Calibration
Diverse Ensembles Improve Calibration
Asa Cooper Stickland
Iain Murray
UQCV
FedML
34
26
0
08 Jul 2020
Simple and Principled Uncertainty Estimation with Deterministic Deep
  Learning via Distance Awareness
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Jeremiah Zhe Liu
Zi Lin
Shreyas Padhy
Dustin Tran
Tania Bedrax-Weiss
Balaji Lakshminarayanan
UQCV
BDL
103
443
0
17 Jun 2020
BatchEnsemble: An Alternative Approach to Efficient Ensemble and
  Lifelong Learning
BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning
Yeming Wen
Dustin Tran
Jimmy Ba
OOD
FedML
UQCV
103
487
0
17 Feb 2020
Deep Ensembles: A Loss Landscape Perspective
Deep Ensembles: A Loss Landscape Perspective
Stanislav Fort
Huiyi Hu
Balaji Lakshminarayanan
OOD
UQCV
65
624
0
05 Dec 2019
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
J. Liu
John Paisley
M. Kioumourtzoglou
B. Coull
UQCV
UD
PER
58
84
0
11 Nov 2019
Analyzing the Role of Model Uncertainty for Electronic Health Records
Analyzing the Role of Model Uncertainty for Electronic Health Records
Michael W. Dusenberry
Dustin Tran
Edward Choi
Jonas Kemp
Jeremy Nixon
Ghassen Jerfel
Katherine A. Heller
Andrew M. Dai
40
118
0
10 Jun 2019
Can You Trust Your Model's Uncertainty? Evaluating Predictive
  Uncertainty Under Dataset Shift
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Yaniv Ovadia
Emily Fertig
Jie Jessie Ren
Zachary Nado
D. Sculley
Sebastian Nowozin
Joshua V. Dillon
Balaji Lakshminarayanan
Jasper Snoek
UQCV
137
1,677
0
06 Jun 2019
MixMatch: A Holistic Approach to Semi-Supervised Learning
MixMatch: A Holistic Approach to Semi-Supervised Learning
David Berthelot
Nicholas Carlini
Ian Goodfellow
Nicolas Papernot
Avital Oliver
Colin Raffel
121
3,009
0
06 May 2019
Can You Trust This Prediction? Auditing Pointwise Reliability After
  Learning
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning
Peter F. Schulam
Suchi Saria
OOD
57
103
0
02 Jan 2019
Tight Prediction Intervals Using Expanded Interval Minimization
Tight Prediction Intervals Using Expanded Interval Minimization
Dongqi Su
Ying Yin Ting
Jason Ansel
UQCV
34
12
0
28 Jun 2018
Deep Reinforcement Learning in a Handful of Trials using Probabilistic
  Dynamics Models
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
Kurtland Chua
Roberto Calandra
R. McAllister
Sergey Levine
BDL
160
1,263
0
30 May 2018
To Trust Or Not To Trust A Classifier
To Trust Or Not To Trust A Classifier
Heinrich Jiang
Been Kim
Melody Y. Guan
Maya R. Gupta
UQCV
110
469
0
30 May 2018
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep
  Networks for Thompson Sampling
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
C. Riquelme
George Tucker
Jasper Snoek
BDL
55
365
0
26 Feb 2018
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate
  Modeling and Uncertainty Quantification
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification
Yinhao Zhu
N. Zabaras
UQCV
BDL
54
640
0
21 Jan 2018
Training Confidence-calibrated Classifiers for Detecting
  Out-of-Distribution Samples
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
Kimin Lee
Honglak Lee
Kibok Lee
Jinwoo Shin
OODD
98
880
0
26 Nov 2017
On Calibration of Modern Neural Networks
On Calibration of Modern Neural Networks
Chuan Guo
Geoff Pleiss
Yu Sun
Kilian Q. Weinberger
UQCV
191
5,774
0
14 Jun 2017
Snapshot Ensembles: Train 1, get M for free
Snapshot Ensembles: Train 1, get M for free
Gao Huang
Yixuan Li
Geoff Pleiss
Zhuang Liu
John E. Hopcroft
Kilian Q. Weinberger
OOD
FedML
UQCV
110
938
0
01 Apr 2017
Multiplicative Normalizing Flows for Variational Bayesian Neural
  Networks
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
Christos Louizos
Max Welling
BDL
129
456
0
06 Mar 2017
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
468
5,748
0
05 Dec 2016
Gaussian Error Linear Units (GELUs)
Gaussian Error Linear Units (GELUs)
Dan Hendrycks
Kevin Gimpel
153
4,934
0
27 Jun 2016
Fast and Accurate Deep Network Learning by Exponential Linear Units
  (ELUs)
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Djork-Arné Clevert
Thomas Unterthiner
Sepp Hochreiter
203
5,502
0
23 Nov 2015
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
457
9,233
0
06 Jun 2015
Weight Uncertainty in Neural Networks
Weight Uncertainty in Neural Networks
Charles Blundell
Julien Cornebise
Koray Kavukcuoglu
Daan Wierstra
UQCV
BDL
109
1,878
0
20 May 2015
Scalable Bayesian Optimization Using Deep Neural Networks
Scalable Bayesian Optimization Using Deep Neural Networks
Jasper Snoek
Oren Rippel
Kevin Swersky
Ryan Kiros
N. Satish
N. Sundaram
Md. Mostofa Ali Patwary
P. Prabhat
Ryan P. Adams
BDL
UQCV
64
1,039
0
19 Feb 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
776
149,474
0
22 Dec 2014
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