Sorting out Lipschitz function approximation

13 November 2018

Papers citing "Sorting out Lipschitz function approximation"

26 / 76 papers shown

Title
Neural Network Robustness as a Verification Property: A Principled Case Study Marco Casadio Ekaterina Komendantskaya M. Daggitt Wen Kokke Guy Katz Guy Amir Idan Refaeli OOD AAML 19 39 0 03 Apr 2021
Learning to Optimize: A Primer and A Benchmark Tianlong Chen Xiaohan Chen Wuyang Chen Howard Heaton Jialin Liu Zhangyang Wang W. Yin 54 225 0 23 Mar 2021
JFB: Jacobian-Free Backpropagation for Implicit Networks Samy Wu Fung Howard Heaton Qiuwei Li Daniel McKenzie Stanley Osher W. Yin FedML 35 84 0 23 Mar 2021
CLIP: Cheap Lipschitz Training of Neural Networks Leon Bungert René Raab Tim Roith Leo Schwinn Daniel Tenbrinck 32 32 0 23 Mar 2021
Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance) Jan Stanczuk Christian Etmann L. Kreusser Carola-Bibiane Schönlieb GAN 16 48 0 02 Mar 2021
Globally-Robust Neural Networks Klas Leino Zifan Wang Matt Fredrikson AAML OOD 80 126 0 16 Feb 2021
Low Curvature Activations Reduce Overfitting in Adversarial Training Vasu Singla Sahil Singla David Jacobs S. Feizi AAML 34 45 0 15 Feb 2021
On the human-recognizability phenomenon of adversarially trained deep image classifiers Jonathan W. Helland Nathan M. VanHoudnos AAML 27 4 0 18 Dec 2020
Incorporating Interpretable Output Constraints in Bayesian Neural Networks Wanqian Yang Lars Lorch Moritz Graule Himabindu Lakkaraju Finale Doshi-Velez UQCV BDL 17 16 0 21 Oct 2020
Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors Yu Sun Jiaming Liu Yiran Sun B. Wohlberg Ulugbek S. Kamilov 37 15 0 03 Oct 2020
A Hölderian backtracking method for min-max and min-min problems Jérôme Bolte Lilian E. Glaudin Edouard Pauwels M. Serrurier 29 9 0 17 Jul 2020
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 37 437 0 17 Jun 2020
The Lipschitz Constant of Self-Attention Hyunjik Kim George Papamakarios A. Mnih 14 134 0 08 Jun 2020
An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality Silviu Pitis Harris Chan Kiarash Jamali Jimmy Ba 8 24 0 14 Feb 2020
Semialgebraic Optimization for Lipschitz Constants of ReLU Networks Tong Chen J. Lasserre Victor Magron Edouard Pauwels 36 3 0 10 Feb 2020
On the distance between two neural networks and the stability of learning Jeremy Bernstein Arash Vahdat Yisong Yue Xuan Li ODL 200 57 0 09 Feb 2020
Orthogonal Wasserstein GANs J. Müller Reinhard Klein Michael Weinmann 48 9 0 29 Nov 2019
Fantastic Four: Differentiable Bounds on Singular Values of Convolution Layers Sahil Singla S. Feizi AAML 35 8 0 22 Nov 2019
L*ReLU: Piece-wise Linear Activation Functions for Deep Fine-grained Visual Categorization Mina Basirat P. Roth 16 8 0 27 Oct 2019
Defending Against Adversarial Examples with K-Nearest Neighbor Chawin Sitawarin David Wagner AAML 8 29 0 23 Jun 2019
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks Kaifeng Lyu Jian Li 52 324 0 13 Jun 2019
FSPool: Learning Set Representations with Featurewise Sort Pooling Yan Zhang Jonathon S. Hare Adam Prugel-Bennett 19 75 0 06 Jun 2019
Wasserstein Dependency Measure for Representation Learning Sherjil Ozair Corey Lynch Yoshua Bengio Aaron van den Oord Sergey Levine P. Sermanet SSL DRL 30 116 0 28 Mar 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 19 1,995 0 08 Feb 2019
Invertible Residual Networks Jens Behrmann Will Grathwohl Ricky T. Q. Chen David Duvenaud J. Jacobsen UQCV TPM 31 618 0 02 Nov 2018
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks Lechao Xiao Yasaman Bahri Jascha Narain Sohl-Dickstein S. Schoenholz Jeffrey Pennington 244 349 0 14 Jun 2018