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Sparse Deep Learning: A New Framework Immune to Local Traps and
  Miscalibration

Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration

1 October 2021
Y. Sun
Wenjun Xiong
F. Liang
ArXivPDFHTML

Papers citing "Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration"

6 / 6 papers shown
Title
Sparse Deep Learning for Time Series Data: Theory and Applications
Sparse Deep Learning for Time Series Data: Theory and Applications
Mingxuan Zhang
Y. Sun
Faming Liang
AI4TS
OOD
BDL
39
2
0
05 Oct 2023
Density Regression and Uncertainty Quantification with Bayesian Deep
  Noise Neural Networks
Density Regression and Uncertainty Quantification with Bayesian Deep Noise Neural Networks
Daiwei Zhang
Tianci Liu
Jian Kang
BDL
UQCV
35
2
0
12 Jun 2022
Consistent Sparse Deep Learning: Theory and Computation
Consistent Sparse Deep Learning: Theory and Computation
Y. Sun
Qifan Song
F. Liang
BDL
41
27
0
25 Feb 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
276
5,675
0
05 Dec 2016
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
9,145
0
06 Jun 2015
1