ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1605.08283
  4. Cited By
Discrete Deep Feature Extraction: A Theory and New Architectures

Discrete Deep Feature Extraction: A Theory and New Architectures

26 May 2016
Thomas Wiatowski
Michael Tschannen
Aleksandar Stanić
Philipp Grohs
Helmut Bölcskei
    FAtt
ArXivPDFHTML

Papers citing "Discrete Deep Feature Extraction: A Theory and New Architectures"

5 / 5 papers shown
Title
Prevalence of Neural Collapse during the terminal phase of deep learning
  training
Prevalence of Neural Collapse during the terminal phase of deep learning training
Vardan Papyan
Xuemei Han
D. Donoho
35
549
0
18 Aug 2020
Regularization via Mass Transportation
Regularization via Mass Transportation
Soroosh Shafieezadeh-Abadeh
Daniel Kuhn
Peyman Mohajerin Esfahani
OOD
24
203
0
27 Oct 2017
Basic Filters for Convolutional Neural Networks Applied to Music:
  Training or Design?
Basic Filters for Convolutional Neural Networks Applied to Music: Training or Design?
M. Dörfler
Thomas Grill
Roswitha Bammer
A. Flexer
20
7
0
07 Sep 2017
Deep Structured Features for Semantic Segmentation
Deep Structured Features for Semantic Segmentation
Michael Tschannen
Lukas Cavigelli
Fabian Mentzer
Thomas Wiatowski
Luca Benini
SSeg
22
14
0
26 Sep 2016
A Mathematical Theory of Deep Convolutional Neural Networks for Feature
  Extraction
A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction
Thomas Wiatowski
Helmut Bölcskei
FAtt
34
363
0
19 Dec 2015
1