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. 1712.07834
  4. Cited By
DropMax: Adaptive Variational Softmax

DropMax: Adaptive Variational Softmax

21 December 2017
Haebeom Lee
Juho Lee
Saehoon Kim
Eunho Yang
Sung Ju Hwang
ArXivPDFHTML

Papers citing "DropMax: Adaptive Variational Softmax"

5 / 5 papers shown
Title
Ghost Loss to Question the Reliability of Training Data
Ghost Loss to Question the Reliability of Training Data
A. Deliège
A. Cioppa
Marc Van Droogenbroeck
UQCV
21
1
0
03 Sep 2021
Why have a Unified Predictive Uncertainty? Disentangling it using Deep
  Split Ensembles
Why have a Unified Predictive Uncertainty? Disentangling it using Deep Split Ensembles
U. Sarawgi
W. Zulfikar
Rishab Khincha
Pattie Maes
PER
UQCV
BDL
UD
21
7
0
25 Sep 2020
Towards Scalable, Efficient and Accurate Deep Spiking Neural Networks
  with Backward Residual Connections, Stochastic Softmax and Hybridization
Towards Scalable, Efficient and Accurate Deep Spiking Neural Networks with Backward Residual Connections, Stochastic Softmax and Hybridization
Priyadarshini Panda
Sai Aparna Aketi
Kaushik Roy
18
35
0
30 Oct 2019
Bayesian Convolutional Neural Networks with Bernoulli Approximate
  Variational Inference
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
Y. Gal
Zoubin Ghahramani
UQCV
BDL
202
745
0
06 Jun 2015
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
287
9,145
0
06 Jun 2015
1