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On Connecting Deep Trigonometric Networks with Deep Gaussian Processes:
  Covariance, Expressivity, and Neural Tangent Kernel

On Connecting Deep Trigonometric Networks with Deep Gaussian Processes: Covariance, Expressivity, and Neural Tangent Kernel

14 March 2022
Chi-Ken Lu
Patrick Shafto
    BDL
ArXivPDFHTML

Papers citing "On Connecting Deep Trigonometric Networks with Deep Gaussian Processes: Covariance, Expressivity, and Neural Tangent Kernel"

4 / 4 papers shown
Title
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems
Anh Tong
Jaesik Choi
21
2
0
19 Oct 2020
Why bigger is not always better: on finite and infinite neural networks
Why bigger is not always better: on finite and infinite neural networks
Laurence Aitchison
173
51
0
17 Oct 2019
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
270
5,660
0
05 Dec 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
282
9,136
0
06 Jun 2015
1