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. 1805.07624
  4. Cited By
Nonparametric Bayesian Deep Networks with Local Competition

Nonparametric Bayesian Deep Networks with Local Competition

19 May 2018
Konstantinos P. Panousis
S. Chatzis
Sergios Theodoridis
    BDL
ArXivPDFHTML

Papers citing "Nonparametric Bayesian Deep Networks with Local Competition"

7 / 7 papers shown
Title
Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive Learning
Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive Learning
Andrei Semenov
Vladimir Ivanov
Aleksandr Beznosikov
Alexander Gasnikov
29
6
0
04 Apr 2024
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic
  Meta-Learning
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning
Konstantinos Kalais
S. Chatzis
BDL
19
8
0
02 Aug 2022
Rethinking Bayesian Learning for Data Analysis: The Art of Prior and
  Inference in Sparsity-Aware Modeling
Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling
Lei Cheng
Feng Yin
Sergios Theodoridis
S. Chatzis
Tsung-Hui Chang
60
74
0
28 May 2022
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial
  Robustness
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness
Konstantinos P. Panousis
S. Chatzis
Sergios Theodoridis
BDL
AAML
58
11
0
05 Dec 2021
Stochastic Transformer Networks with Linear Competing Units: Application
  to end-to-end SL Translation
Stochastic Transformer Networks with Linear Competing Units: Application to end-to-end SL Translation
Andreas Voskou
Konstantinos P. Panousis
D. Kosmopoulos
Dimitris N. Metaxas
S. Chatzis
SLR
20
43
0
01 Sep 2021
Local Competition and Stochasticity for Adversarial Robustness in Deep
  Learning
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning
Konstantinos P. Panousis
S. Chatzis
Antonios Alexos
Sergios Theodoridis
BDL
AAML
OOD
56
19
0
04 Jan 2021
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
261
9,134
0
06 Jun 2015
1