Mad Max: Affine Spline Insights into Deep Learning

17 May 2018

Richard Baraniuk

Papers citing "Mad Max: Affine Spline Insights into Deep Learning"

21 / 21 papers shown

Title
Free-Knots Kolmogorov-Arnold Network: On the Analysis of Spline Knots and Advancing Stability L. Zheng W. Zhang Lin Yue Miao Xu Olaf Maennel Weitong Chen 54 1 0 17 Jan 2025
On the Geometry of Deep Learning Randall Balestriero Ahmed Imtiaz Humayun Richard G. Baraniuk AI4CE 39 1 0 09 Aug 2024
Reasoning in Large Language Models: A Geometric Perspective Romain Cosentino Sarath Shekkizhar LRM 44 2 0 02 Jul 2024
A max-affine spline approximation of neural networks using the Legendre transform of a convex-concave representation Adam Perrett Danny Wood Gavin Brown 19 0 0 16 Jul 2023
Unsupervised Discovery of Extreme Weather Events Using Universal Representations of Emergent Organization Adam T. Rupe K. Kashinath Nalini Kumar James P. Crutchfield 11 0 0 25 Apr 2023
SpecXAI -- Spectral interpretability of Deep Learning Models Stefan Druc Peter Wooldridge A. Krishnamurthy S. Sarkar Aditya Balu 17 0 0 20 Feb 2023
Deep Learning Meets Sparse Regularization: A Signal Processing Perspective Rahul Parhi Robert D. Nowak 21 25 0 23 Jan 2023
Interpreting Neural Networks through the Polytope Lens Sid Black Lee D. Sharkey Léo Grinsztajn Eric Winsor Daniel A. Braun ... Kip Parker Carlos Ramón Guevara Beren Millidge Gabriel Alfour Connor Leahy FAtt MILM 26 22 0 22 Nov 2022
Towards Global Neural Network Abstractions with Locally-Exact Reconstruction Edoardo Manino I. Bessa Lucas C. Cordeiro 19 1 0 21 Oct 2022
Neural Networks are Decision Trees Çağlar Aytekin FAtt 32 24 0 11 Oct 2022
Batch Normalization Explained Randall Balestriero Richard G. Baraniuk AAML 28 16 0 29 Sep 2022
ExSpliNet: An interpretable and expressive spline-based neural network Daniele Fakhoury Emanuele Fakhoury H. Speleers 11 33 0 03 May 2022
Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values Ahmed Imtiaz Humayun Randall Balestriero Richard Baraniuk 11 31 0 03 Mar 2022
What Kinds of Functions do Deep Neural Networks Learn? Insights from Variational Spline Theory Rahul Parhi Robert D. Nowak MLT 27 70 0 07 May 2021
Fast Jacobian-Vector Product for Deep Networks Randall Balestriero Richard Baraniuk 23 4 0 01 Apr 2021
Time Series Simulation by Conditional Generative Adversarial Net Rao Fu Jie Chen Shutian Zeng Yiping Zhuang Agus Sudjianto AI4TS OOD GAN 22 47 0 25 Apr 2019
Input Convex Neural Networks Brandon Amos Lei Xu J. Zico Kolter 175 597 0 22 Sep 2016
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding V. Papyan Yaniv Romano Michael Elad 56 284 0 27 Jul 2016
Piecewise convexity of artificial neural networks Blaine Rister Daniel L Rubin AAML ODL 26 31 0 17 Jul 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 281 5,833 0 08 Jul 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015