Fetching the paper…
Reading the bibliography…
We provide simple schemes to build Bayesian Neural Networks (BNNs), block by block, inspired by a recent idea of computation skeletons.
Invertibility of random matrices: norm of the inverse
Mark Rudelson · 2008
Earlier work this paper cites.
Kernel methods for deep learning
Youngmin Cho and Lawrence K Saul · 2009
Earlier work this paper cites.
Bart: Bayesian additive regression trees
Hugh A Chipman, Edward I George, Robert E McCulloch, et al · 2010
Earlier work this paper cites.
Practical variational inference for neural networks
Alex Graves · 2011
Earlier work this paper cites.
Bayesian learning for neural networks
Radford M Neal · 2012
Earlier work this paper cites.
Topics in random matrix theory
Terence Tao · 2012
Earlier work this paper cites.
Invariant scattering convolution networks
Joan Bruna and Stéphane Mallat · 2013
Earlier work this paper cites.
Deep gaussian processes
Andreas Damianou and Neil Lawrence · 2013
Earlier work this paper cites.
Nested variational compression in deep gaussian processes
James Hensman and Neil D Lawrence · 2014
Earlier work this paper cites.
Expectation propagation for neural networks with sparsity-promoting priors
Pasi Jylänki, Aapo Nummenmaa, and Aki Vehtari · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
Cited alongside, same era.
Variational auto-encoded deep gaussian processes
Zhenwen Dai, Andreas Damianou, Javier González, and Neil Lawrence · 2015
Cited alongside, same era.
Probabilistic backpropagation for scalable learning of bayesian neural networks
José Miguel Hernández-Lobato and Ryan Adams · 2015
Cited alongside, same era.
Probabilistic backpropagation for scalable learning of bayesian neural networks
José Miguel Hernández-Lobato and Ryan Adams · 2015
Cited alongside, same era.
Deep gaussian processes for regression using approximate expectation propagation
Thang Bui, Daniel Hernández-Lobato, Jose Hernandez-Lobato, Yingzhen Li, and Richard Turner · 2016
Cited alongside, same era.
Variational inference: A review for statisticians
David M Blei, Alp Kucukelbir, and Jon D McAuliffe · 2017
Later among the works it cites.
Random feature expansions for deep gaussian processes
Kurt Cutajar, Edwin V Bonilla, Pietro Michiardi, and Maurizio Filippone · 2017
Later among the works it cites.
Dynamic routing between capsules
Sara Sabour, Nicholas Frosst, and Geoffrey E Hinton · 2017
Later among the works it cites.
Doubly stochastic variational inference for deep gaussian processes
Hugh Salimbeni and Marc Deisenroth · 2017
Later among the works it cites.
Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
Later among the works it cites.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Toward deeper understanding of neural networks: The power of initialization and a dual view on expressivity
Amit Daniely, Roy Frostig, and Yoram Singer · 2016
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Cited alongside, same era.
Understanding deep convolutional networks
Stéphane Mallat · 2016
Cited alongside, same era.
Deep kernel learning
Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, and Eric P Xing · 2016
Cited alongside, same era.
Towards better understanding of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross · 2018
Closest in time.
Generative networks as inverse problems with scattering transforms
Tomás Angles and Stéphane Mallat · 2018
Closest in time.
Detecting statistical interactions from neural network weights
Michael Tsang, Dehua Cheng, and Yan Liu · 2018
Closest in time.