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Towards Global Neural Network Abstractions with Locally-Exact
  Reconstruction
v1v2 (latest)

Towards Global Neural Network Abstractions with Locally-Exact Reconstruction

21 October 2022
Edoardo Manino
I. Bessa
Lucas C. Cordeiro
ArXiv (abs)PDFHTML

Papers citing "Towards Global Neural Network Abstractions with Locally-Exact Reconstruction"

19 / 19 papers shown
Title
The Second International Verification of Neural Networks Competition
  (VNN-COMP 2021): Summary and Results
The Second International Verification of Neural Networks Competition (VNN-COMP 2021): Summary and Results
Stanley Bak
Changliu Liu
Taylor T. Johnson
NAI
91
112
0
31 Aug 2021
On the Effect of Pruning on Adversarial Robustness
On the Effect of Pruning on Adversarial Robustness
Artur Jordão
Hélio Pedrini
AAML
81
23
0
10 Aug 2021
MLPerf Tiny Benchmark
MLPerf Tiny Benchmark
Colby R. Banbury
Vijay Janapa Reddi
P. Torelli
J. Holleman
Nat Jeffries
...
Videet Parekh
Honson Tran
Nhan Tran
Niu Wenxu
Xu Xuesong
VLM
99
190
0
14 Jun 2021
Privacy-Preserving Machine Learning with Fully Homomorphic Encryption
  for Deep Neural Network
Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network
Joon-Woo Lee
Hyungchul Kang
Yongwoo Lee
W. Choi
Jieun Eom
...
Eunsang Lee
Junghyun Lee
Donghoon Yoo
Young-Sik Kim
Jong-Seon No
80
251
0
14 Jun 2021
PRIMA: General and Precise Neural Network Certification via Scalable
  Convex Hull Approximations
PRIMA: General and Precise Neural Network Certification via Scalable Convex Hull Approximations
Mark Niklas Muller
Gleb Makarchuk
Gagandeep Singh
Markus Püschel
Martin Vechev
65
92
0
05 Mar 2021
Pruning and Quantization for Deep Neural Network Acceleration: A Survey
Pruning and Quantization for Deep Neural Network Acceleration: A Survey
Tailin Liang
C. Glossner
Lei Wang
Shaobo Shi
Xiaotong Zhang
MQ
208
700
0
24 Jan 2021
An Abstraction-Based Framework for Neural Network Verification
An Abstraction-Based Framework for Neural Network Verification
Y. Elboher
Justin Emile Gottschlich
Guy Katz
106
127
0
31 Oct 2019
ReachNN: Reachability Analysis of Neural-Network Controlled Systems
ReachNN: Reachability Analysis of Neural-Network Controlled Systems
Chao Huang
Jiameng Fan
Wenchao Li
Xin Chen
Qi Zhu
58
79
0
25 Jun 2019
Equivalent and Approximate Transformations of Deep Neural Networks
Equivalent and Approximate Transformations of Deep Neural Networks
Abhinav Kumar
Thiago Serra
Srikumar Ramalingam
64
21
0
27 May 2019
Defensive Quantization: When Efficiency Meets Robustness
Defensive Quantization: When Efficiency Meets Robustness
Ji Lin
Chuang Gan
Song Han
MQ
77
203
0
17 Apr 2019
Algorithms for Verifying Deep Neural Networks
Algorithms for Verifying Deep Neural Networks
Changliu Liu
Tomer Arnon
Christopher Lazarus
Christopher A. Strong
Clark W. Barrett
Mykel J. Kochenderfer
AAML
96
400
0
15 Mar 2019
A Convex Relaxation Barrier to Tight Robustness Verification of Neural
  Networks
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks
Hadi Salman
Greg Yang
Huan Zhang
Cho-Jui Hsieh
Pengchuan Zhang
AAML
114
271
0
23 Feb 2019
ResNet with one-neuron hidden layers is a Universal Approximator
ResNet with one-neuron hidden layers is a Universal Approximator
Hongzhou Lin
Stefanie Jegelka
105
229
0
28 Jun 2018
Mad Max: Affine Spline Insights into Deep Learning
Mad Max: Affine Spline Insights into Deep Learning
Randall Balestriero
Richard Baraniuk
AI4CE
61
78
0
17 May 2018
Towards Fast Computation of Certified Robustness for ReLU Networks
Towards Fast Computation of Certified Robustness for ReLU Networks
Tsui-Wei Weng
Huan Zhang
Hongge Chen
Zhao Song
Cho-Jui Hsieh
Duane S. Boning
Inderjit S. Dhillon
Luca Daniel
AAML
108
695
0
25 Apr 2018
Stochastic Activation Pruning for Robust Adversarial Defense
Stochastic Activation Pruning for Robust Adversarial Defense
Guneet Singh Dhillon
Kamyar Azizzadenesheli
Zachary Chase Lipton
Jeremy Bernstein
Jean Kossaifi
Aran Khanna
Anima Anandkumar
AAML
81
547
0
05 Mar 2018
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Guy Katz
Clark W. Barrett
D. Dill
Kyle D. Julian
Mykel Kochenderfer
AAML
318
1,874
0
03 Feb 2017
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained
  Quantization and Huffman Coding
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
Song Han
Huizi Mao
W. Dally
3DGS
263
8,859
0
01 Oct 2015
Neural Network with Unbounded Activation Functions is Universal
  Approximator
Neural Network with Unbounded Activation Functions is Universal Approximator
Sho Sonoda
Noboru Murata
70
336
0
14 May 2015
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