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What Do Compressed Deep Neural Networks Forget?
13 November 2019
Sara Hooker
Aaron Courville
Gregory Clark
Yann N. Dauphin
Andrea Frome
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Papers citing
"What Do Compressed Deep Neural Networks Forget?"
50 / 59 papers shown
Title
A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advances
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Composable Interventions for Language Models
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Kyle O'Brien
Tianjin Huang
Shanghua Gao
Shiwei Liu
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Faisal Mahmood
Marinka Zitnik
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Robustness-Reinforced Knowledge Distillation with Correlation Distance and Network Pruning
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What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation
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Jonathan Frankle
John Guttag
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Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers
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Eric Wallace
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Kurt Keutzer
Dan Klein
Joseph E. Gonzalez
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26 Feb 2020
MLIR: A Compiler Infrastructure for the End of Moore's Law
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M. Amini
Uday Bondhugula
Albert Cohen
Andy Davis
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Rigging the Lottery: Making All Tickets Winners
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Jacob Menick
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Natural Adversarial Examples
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Kevin Zhao
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Towards Compact and Robust Deep Neural Networks
Vikash Sehwag
Shiqi Wang
Prateek Mittal
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Does Learning Require Memorization? A Short Tale about a Long Tail
Vitaly Feldman
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Energy and Policy Considerations for Deep Learning in NLP
Emma Strubell
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Andrew McCallum
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Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
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Thomas G. Dietterich
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The State of Sparsity in Deep Neural Networks
Trevor Gale
Erich Elsen
Sara Hooker
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Fair Regression for Health Care Spending
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Sherri Rose
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LPCNet: Improving Neural Speech Synthesis Through Linear Prediction
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Jan Skoglund
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Sparse DNNs with Improved Adversarial Robustness
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Chao Zhang
Changshui Zhang
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SNIP: Single-shot Network Pruning based on Connection Sensitivity
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Philip Torr
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Metric Learning for Novelty and Anomaly Detection
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Idoia Ruiz
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Antonio M. López
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Confounding variables can degrade generalization performance of radiological deep learning models
J. Zech
Marcus A. Badgeley
Manway Liu
A. Costa
J. Titano
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02 Jul 2018
A Benchmark for Interpretability Methods in Deep Neural Networks
Sara Hooker
D. Erhan
Pieter-Jan Kindermans
Been Kim
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Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach
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Ryan P. Adams
Peter Orbanz
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Efficient Neural Audio Synthesis
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Erich Elsen
Karen Simonyan
Seb Noury
Norman Casagrande
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Florian Stimberg
Aaron van den Oord
Sander Dieleman
Koray Kavukcuoglu
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Faster gaze prediction with dense networks and Fisher pruning
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I. Korshunova
Alykhan Tejani
Ferenc Huszár
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17 Jan 2018
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
Benoit Jacob
S. Kligys
Bo Chen
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Matthew Tang
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Dmitry Kalenichenko
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167
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15 Dec 2017
Learning Sparse Neural Networks through
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Christos Louizos
Max Welling
Diederik P. Kingma
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MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks
A. Gordon
Elad Eban
Ofir Nachum
Bo Chen
Hao Wu
Tien-Ju Yang
Edward Choi
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Mixed Precision Training
Paulius Micikevicius
Sharan Narang
Jonah Alben
G. Diamos
Erich Elsen
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Boris Ginsburg
Michael Houston
Oleksii Kuchaiev
Ganesh Venkatesh
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To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu
Suyog Gupta
202
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Learning Efficient Convolutional Networks through Network Slimming
Zhuang Liu
Jianguo Li
Zhiqiang Shen
Gao Huang
Shoumeng Yan
Changshui Zhang
133
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22 Aug 2017
On Calibration of Modern Neural Networks
Chuan Guo
Geoff Pleiss
Yu Sun
Kilian Q. Weinberger
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299
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14 Jun 2017
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
Shiyu Liang
Yixuan Li
R. Srikant
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Exploring Sparsity in Recurrent Neural Networks
Sharan Narang
Erich Elsen
G. Diamos
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew G. Howard
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What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
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Online Learning with Abstention
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Giulia DeSalvo
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M. Mohri
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114
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Soft Weight-Sharing for Neural Network Compression
Karen Ullrich
Edward Meeds
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172
419
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Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova
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302
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A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
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Kevin Gimpel
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179
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Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations
Itay Hubara
Matthieu Courbariaux
Daniel Soudry
Ran El-Yaniv
Yoshua Bengio
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Dynamic Network Surgery for Efficient DNNs
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Anbang Yao
Yurong Chen
84
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Learning Structured Sparsity in Deep Neural Networks
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Chunpeng Wu
Yandan Wang
Yiran Chen
Hai Helen Li
196
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On the efficient representation and execution of deep acoustic models
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Compression of Neural Machine Translation Models via Pruning
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Christopher D. Manning
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220
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Satisfying Real-world Goals with Dataset Constraints
Gabriel Goh
Andrew Cotter
Maya R. Gupta
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Eugenio Culurciello
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