Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales
Search
Open menu
Home
Papers
2001.06263
Cited By
v1
v2 (latest)
Deep Neural Networks with Trainable Activations and Controlled Lipschitz Constant
IEEE Transactions on Signal Processing (TSP), 2020
17 January 2020
Shayan Aziznejad
Harshit Gupta
Joaquim Campos
M. Unser
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"Deep Neural Networks with Trainable Activations and Controlled Lipschitz Constant"
22 / 22 papers shown
Developing Training Procedures for Piecewise-linear Spline Activation Functions in Neural Networks
William H Patty
LLMSV
134
0
0
17 Sep 2025
L-Lipschitz Gershgorin ResNet Network
Marius F. R. Juston
William R. Norris
William R. Norris
A. Soylemezoglu
276
0
0
28 Feb 2025
DAREK -- Distance Aware Error for Kolmogorov Networks
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2025
Masoud Ataei
M. J. Khojasteh
Vikas Dhiman
163
1
0
10 Jan 2025
A Tunable Despeckling Neural Network Stabilized via Diffusion Equation
Signal Processing (Signal Process.), 2024
Yi Ran
Zhichang Guo
Jia Li
Yao Li
Martin Burger
Boying Wu
DiffM
307
0
0
24 Nov 2024
1-Lipschitz Neural Networks are more expressive with N-Activations
Bernd Prach
Christoph H. Lampert
AAML
FAtt
228
1
0
10 Nov 2023
Deep Stochastic Mechanics
International Conference on Machine Learning (ICML), 2023
Elena Orlova
Aleksei Ustimenko
Ruoxi Jiang
Peter Y. Lu
Rebecca Willett
DiffM
479
1
0
31 May 2023
How Does Information Bottleneck Help Deep Learning?
International Conference on Machine Learning (ICML), 2023
Kenji Kawaguchi
Zhun Deng
Xu Ji
Jiaoyang Huang
220
105
0
30 May 2023
Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz
IEEE journal of biomedical and health informatics (IEEE JBHI), 2023
D. Bhutto
Bo Zhu
J. Liu
Neha Koonjoo
H. Li
Bruce Rosen
Matthew S. Rosen
UQCV
OOD
389
2
0
12 May 2023
When Deep Learning Meets Polyhedral Theory: A Survey
Joey Huchette
Gonzalo Muñoz
Thiago Serra
Calvin Tsay
AI4CE
674
45
0
29 Apr 2023
Learning Gradually Non-convex Image Priors Using Score Matching
Erich Kobler
Thomas Pock
254
5
0
21 Feb 2023
Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions
Journal of machine learning research (JMLR), 2022
Stanislas Ducotterd
Alexis Goujon
Pakshal Bohra
Dimitris Perdios
Sebastian Neumayer
M. Unser
247
19
0
28 Oct 2022
Analyzing Explainer Robustness via Probabilistic Lipschitzness of Prediction Functions
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Zulqarnain Khan
Davin Hill
A. Masoomi
Joshua Bone
Jennifer Dy
AAML
391
7
0
24 Jun 2022
Approximation of Lipschitz Functions using Deep Spline Neural Networks
SIAM Journal on Mathematics of Data Science (SIMODS), 2022
Sebastian Neumayer
Alexis Goujon
Pakshal Bohra
M. Unser
153
17
0
13 Apr 2022
A Quantitative Geometric Approach to Neural-Network Smoothness
Neural Information Processing Systems (NeurIPS), 2022
Zehao Wang
Gautam Prakriya
S. Jha
318
16
0
02 Mar 2022
CNN-based regularisation for CT image reconstructions
Attila Juhos
MedIm
93
0
0
22 Jan 2022
Sparsest Univariate Learning Models Under Lipschitz Constraint
IEEE Open Journal of Signal Processing (JOSP), 2021
Shayan Aziznejad
Thomas Debarre
M. Unser
178
4
0
27 Dec 2021
Measuring Complexity of Learning Schemes Using Hessian-Schatten Total Variation
Shayan Aziznejad
Joaquim Campos
M. Unser
253
12
0
12 Dec 2021
Provable Lipschitz Certification for Generative Models
Matt Jordan
A. Dimakis
112
14
0
06 Jul 2021
Augmented Shortcuts for Vision Transformers
Neural Information Processing Systems (NeurIPS), 2021
Yehui Tang
Kai Han
Chang Xu
An Xiao
Yiping Deng
Chao Xu
Yunhe Wang
ViT
253
48
0
30 Jun 2021
What Kinds of Functions do Deep Neural Networks Learn? Insights from Variational Spline Theory
SIAM Journal on Mathematics of Data Science (SIMODS), 2021
Rahul Parhi
Robert D. Nowak
MLT
412
76
0
07 May 2021
Learning Lipschitz Feedback Policies from Expert Demonstrations: Closed-Loop Guarantees, Generalization and Robustness
Abed AlRahman Al Makdah
Vishaal Krishnan
Fabio Pasqualetti
224
0
0
30 Mar 2021
CLIP: Cheap Lipschitz Training of Neural Networks
Scale Space and Variational Methods in Computer Vision (SSVM), 2021
Leon Bungert
René Raab
Tim Roith
Leo Schwinn
Daniel Tenbrinck
162
39
0
23 Mar 2021
1
Page 1 of 1
