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Selective Classification Via Neural Network Training Dynamics
v1v2v3 (latest)

Selective Classification Via Neural Network Training Dynamics

26 May 2022
Stephan Rabanser
Anvith Thudi
Kimia Hamidieh
Adam Dziedzic
Nicolas Papernot
ArXiv (abs)PDFHTML

Papers citing "Selective Classification Via Neural Network Training Dynamics"

30 / 30 papers shown
Title
On the Necessity of Auditable Algorithmic Definitions for Machine
  Unlearning
On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning
Anvith Thudi
Hengrui Jia
Ilia Shumailov
Nicolas Papernot
MU
63
159
0
22 Oct 2021
HODA: Hardness-Oriented Detection of Model Extraction Attacks
HODA: Hardness-Oriented Detection of Model Extraction Attacks
A. M. Sadeghzadeh
Amir Mohammad Sobhanian
F. Dehghan
R. Jalili
MIACV
58
7
0
21 Jun 2021
Deep Learning Through the Lens of Example Difficulty
Deep Learning Through the Lens of Example Difficulty
R. Baldock
Hartmut Maennel
Behnam Neyshabur
83
161
0
17 Jun 2021
Generating and Characterizing Scenarios for Safety Testing of Autonomous
  Vehicles
Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles
Zahra Ghodsi
S. Hari
I. Frosio
Timothy Tsai
Alejandro J. Troccoli
S. Keckler
S. Garg
Anima Anandkumar
68
43
0
12 Mar 2021
Selective Classification Can Magnify Disparities Across Groups
Selective Classification Can Magnify Disparities Across Groups
Erik Jones
Shiori Sagawa
Pang Wei Koh
Ananya Kumar
Percy Liang
88
47
0
27 Oct 2020
Selective Classification via One-Sided Prediction
Selective Classification via One-Sided Prediction
Aditya Gangrade
Anil Kag
Venkatesh Saligrama
UQCV
70
36
0
15 Oct 2020
Energy-based Out-of-distribution Detection
Energy-based Out-of-distribution Detection
Weitang Liu
Xiaoyun Wang
John Douglas Owens
Yixuan Li
OODD
271
1,371
0
08 Oct 2020
Estimating Example Difficulty Using Variance of Gradients
Estimating Example Difficulty Using Variance of Gradients
Chirag Agarwal
Daniel D'souza
Sara Hooker
251
111
0
26 Aug 2020
What Neural Networks Memorize and Why: Discovering the Long Tail via
  Influence Estimation
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation
Vitaly Feldman
Chiyuan Zhang
TDI
207
470
0
09 Aug 2020
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses
Raef Bassily
Vitaly Feldman
Cristóbal Guzmán
Kunal Talwar
MLT
54
198
0
12 Jun 2020
Consistent Estimators for Learning to Defer to an Expert
Consistent Estimators for Learning to Defer to an Expert
Hussein Mozannar
David Sontag
61
204
0
02 Jun 2020
Self-Adaptive Training: beyond Empirical Risk Minimization
Self-Adaptive Training: beyond Empirical Risk Minimization
Lang Huang
Chaoning Zhang
Hongyang R. Zhang
NoLa
84
205
0
24 Feb 2020
Characterizing Structural Regularities of Labeled Data in
  Overparameterized Models
Characterizing Structural Regularities of Labeled Data in Overparameterized Models
Ziheng Jiang
Chiyuan Zhang
Kunal Talwar
Michael C. Mozer
TDI
59
104
0
08 Feb 2020
What Do Compressed Deep Neural Networks Forget?
What Do Compressed Deep Neural Networks Forget?
Sara Hooker
Aaron Courville
Gregory Clark
Yann N. Dauphin
Andrea Frome
87
185
0
13 Nov 2019
Deep Gamblers: Learning to Abstain with Portfolio Theory
Deep Gamblers: Learning to Abstain with Portfolio Theory
Liu Ziyin
Zhikang T. Wang
Paul Pu Liang
Ruslan Salakhutdinov
Louis-Philippe Morency
Masahito Ueda
86
113
0
29 Jun 2019
Selective prediction-set models with coverage guarantees
Selective prediction-set models with coverage guarantees
Jean Feng
A. Sondhi
Jessica Perry
N. Simon
44
8
0
13 Jun 2019
SelectiveNet: A Deep Neural Network with an Integrated Reject Option
SelectiveNet: A Deep Neural Network with an Integrated Reject Option
Yonatan Geifman
Ran El-Yaniv
CVBMOOD
126
311
0
26 Jan 2019
An Empirical Study of Example Forgetting during Deep Neural Network
  Learning
An Empirical Study of Example Forgetting during Deep Neural Network Learning
Mariya Toneva
Alessandro Sordoni
Rémi Tachet des Combes
Adam Trischler
Yoshua Bengio
Geoffrey J. Gordon
128
741
0
12 Dec 2018
A Simple Unified Framework for Detecting Out-of-Distribution Samples and
  Adversarial Attacks
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Kimin Lee
Kibok Lee
Honglak Lee
Jinwoo Shin
OODD
192
2,062
0
10 Jul 2018
Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers
Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers
Yonatan Geifman
Guy Uziel
Ran El-Yaniv
UQCV
57
140
0
21 May 2018
On Calibration of Modern Neural Networks
On Calibration of Modern Neural Networks
Chuan Guo
Geoff Pleiss
Yu Sun
Kilian Q. Weinberger
UQCV
299
5,862
0
14 Jun 2017
Enhancing The Reliability of Out-of-distribution Image Detection in
  Neural Networks
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
Shiyu Liang
Yixuan Li
R. Srikant
UQCVOODD
171
2,081
0
08 Jun 2017
Selective Classification for Deep Neural Networks
Selective Classification for Deep Neural Networks
Yonatan Geifman
Ran El-Yaniv
CVBM
97
529
0
23 May 2017
Active Bias: Training More Accurate Neural Networks by Emphasizing High
  Variance Samples
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples
Haw-Shiuan Chang
Erik Learned-Miller
Andrew McCallum
86
354
0
24 Apr 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
UQCVBDL
842
5,841
0
05 Dec 2016
Unanimous Prediction for 100% Precision with Application to Learning
  Semantic Mappings
Unanimous Prediction for 100% Precision with Application to Learning Semantic Mappings
Fereshte Khani
Martin Rinard
Percy Liang
AAML
43
26
0
20 Jun 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
194,426
0
10 Dec 2015
Train faster, generalize better: Stability of stochastic gradient
  descent
Train faster, generalize better: Stability of stochastic gradient descent
Moritz Hardt
Benjamin Recht
Y. Singer
116
1,243
0
03 Sep 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
UQCVBDL
852
9,346
0
06 Jun 2015
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAttMDE
1.7K
100,508
0
04 Sep 2014
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