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. 1611.08034
  4. Cited By
Scalable Bayesian Learning of Recurrent Neural Networks for Language
  Modeling

Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling

23 November 2016
Zhe Gan
Chunyuan Li
Changyou Chen
Yunchen Pu
Qinliang Su
Lawrence Carin
    BDL
    UQCV
ArXivPDFHTML

Papers citing "Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling"

12 / 12 papers shown
Title
Cooperative Bayesian and variance networks disentangle aleatoric and epistemic uncertainties
Cooperative Bayesian and variance networks disentangle aleatoric and epistemic uncertainties
Jiaxiang Yi
Miguel A. Bessa
UD
PER
UQCV
46
0
0
05 May 2025
Are you using test log-likelihood correctly?
Are you using test log-likelihood correctly?
Sameer K. Deshpande
Soumya K. Ghosh
Tin D. Nguyen
Tamara Broderick
32
7
0
01 Dec 2022
Exploring Predictive Uncertainty and Calibration in NLP: A Study on the
  Impact of Method & Data Scarcity
Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity
Dennis Ulmer
J. Frellsen
Christian Hardmeier
187
22
0
20 Oct 2022
Bayesian Neural Network Language Modeling for Speech Recognition
Bayesian Neural Network Language Modeling for Speech Recognition
Boyang Xue
Shoukang Hu
Junhao Xu
Mengzhe Geng
Xunying Liu
Helen M. Meng
UQCV
BDL
44
14
0
28 Aug 2022
Variational Temporal Deep Generative Model for Radar HRRP Target
  Recognition
Variational Temporal Deep Generative Model for Radar HRRP Target Recognition
D. Guo
Bo Chen
Wenchao Chen
C. Wang
Hongwei Liu
Mingyuan Zhou
BDL
27
35
0
28 Sep 2020
Stein Variational Gradient Descent With Matrix-Valued Kernels
Stein Variational Gradient Descent With Matrix-Valued Kernels
Dilin Wang
Ziyang Tang
Chandrajit L. Bajaj
Qiang Liu
25
62
0
28 Oct 2019
Meta-Learning for Stochastic Gradient MCMC
Meta-Learning for Stochastic Gradient MCMC
Wenbo Gong
Yingzhen Li
José Miguel Hernández-Lobato
BDL
24
44
0
12 Jun 2018
Confidence Modeling for Neural Semantic Parsing
Confidence Modeling for Neural Semantic Parsing
Li Dong
Chris Quirk
Mirella Lapata
24
82
0
11 May 2018
Bayesian Recurrent Neural Network Models for Forecasting and Quantifying
  Uncertainty in Spatial-Temporal Data
Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data
Patrick L. McDermott
C. Wikle
BDL
UQCV
32
96
0
02 Nov 2017
On the Convergence of Stochastic Gradient MCMC Algorithms with
  High-Order Integrators
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators
Changyou Chen
Nan Ding
Lawrence Carin
37
158
0
21 Oct 2016
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
285
9,138
0
06 Jun 2015
Improving neural networks by preventing co-adaptation of feature
  detectors
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton
Nitish Srivastava
A. Krizhevsky
Ilya Sutskever
Ruslan Salakhutdinov
VLM
266
7,636
0
03 Jul 2012
1