From Hard to Soft: Understanding Deep Network Nonlinearities via Vector
Quantization and Statistical Inference

From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference

22 October 2018

Randall Balestriero

Richard G. Baraniuk

Papers citing "From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference"

8 / 8 papers shown

Title
On the Geometry of Deep Learning Randall Balestriero Ahmed Imtiaz Humayun Richard G. Baraniuk AI4CE 80 1 0 09 Aug 2024
Mad Max: Affine Spline Insights into Deep Learning Randall Balestriero Richard Baraniuk AI4CE 49 78 0 17 May 2018
Deep Learning using Rectified Linear Units (ReLU) Abien Fred Agarap 54 3,206 0 22 Mar 2018
Orthogonal Weight Normalization: Solution to Optimization over Multiple Dependent Stiefel Manifolds in Deep Neural Networks Lei Huang Xianglong Liu B. Lang Adams Wei Yu Yongliang Wang Bo Li ODL 58 225 0 16 Sep 2017
Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning Stefan Elfwing E. Uchibe Kenji Doya 57 1,702 0 10 Feb 2017
Neural Photo Editing with Introspective Adversarial Networks Andrew Brock Theodore Lim J. Ritchie Nick Weston GAN 59 458 0 22 Sep 2016
Parametric Exponential Linear Unit for Deep Convolutional Neural Networks Ludovic Trottier Philippe Giguère B. Chaib-draa 57 199 0 30 May 2016
Understanding Locally Competitive Networks R. Srivastava Jonathan Masci Faustino J. Gomez Jürgen Schmidhuber FAtt 39 39 0 05 Oct 2014