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An Updating Survey for Bayesian Deep Learning (BDL)


Towards Bayesian Deep Learning: A Survey
by Wang et al., 2016
[Arxiv Version] and [TKDE Version]

BDL and Recommender Systems

Collaborative Deep Learning for Recommender Systems
by Wang et al., KDD 2015
[PDF] [Project Page] [2014 Arxiv Version]

Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks
by Wang et al., NIPS 2016

Collaborative Knowledge Base Embedding for Recommender Systems
by Zhang et al., KDD 2016

Collaborative Deep Ranking: A Hybrid Pair-Wise Recommendation Algorithm with Implicit Feedback
by Ying et al., PAKDD 2016

Collaborative Variational Autoencoder for Recommender Systems
by Li et al., KDD 2017

Variational Autoencoders for Collaborative Filtering
by Liang et al., WWW 2018

BDL and Healthcare

Electronic Health Record Analysis via Deep Poisson Factor Models
by Henao et al., JMLR 2016

Structured Inference Networks for Nonlinear State Space Models
by Krishnan et al., AAAI 2017

Black Box FDR
by Tansey et al., ICML 2018

Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling
by Wang et al., AAAI 2019

Sampling-free Uncertainty Estimation in Gated Recurrent Units with Applications to Normative Modeling in Neuroimaging
by Hwang et al., UAI 2019


Sequence to Better Sequence: Continuous Revision of Combinatorial Structures
by Mueller et al., ICML 2017

BDL and Computer Vision

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
by Eslami et al., NIPS 2016

Faster Attend-Infer-Repeat with Tractable Probabilistic Models
by Stelzner et al., ICML 2019

BDL and Control

Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
by Watter et al., NIPS 2015

BDL and Graphs (Link Prediction, Graph Neural Networks, etc.)

Relational Deep Learning: A Deep Latent Variable Model for Link Prediction
by Wang et al., AAAI 2017

Graphite: Iterative Generative Modeling of Graphs
by Grover et al., ICML 2019

Relational Variational Autoencoder for Link Prediction with Multimedia Data
by Li et al., ACM MM 2017

Stochastic Blockmodels meet Graph Neural Networks
by Mehta et al., ArXiv 2019

BDL and Topic Modeling

Relational Stacked Denoising Autoencoder for Tag Recommendation
by Wang et al., AAAI 2015

Scalable Deep Poisson Factor Analysis for Topic Modeling
by Gan et al., ICML 2015

Deep Latent Dirichlet Allocation with Topic-layer-adaptive Stochastic Gradient Riemannian MCMC
by Cong et al., ICML 2017

Deep Unfolding for Topic Models
by Chien et al., TPAMI 2017

Neural Relational Topic Models for Scientific Article Analysis
by Bai et al., CIKM 2018

Dirichlet Belief Networks for Topic Structure Learning
by Zhao et al., NIPS 2018

BDL and Speech Recognition

Recurrent Poisson Process Unit for Speech Recognition
by Huang et al., AAAI 2019

BDL as a Framework (Miscellaneous)

Towards Bayesian Deep Learning: A Framework and Some Existing Methods
by Wang et al., TKDE 2016

Composing Graphical Models with Neural Networks for Structured Representations and Fast Inference
by Johnson et al., NIPS 2016

Bayesian/Probabilistic Neural Networks as Building Blocks of BDL

Learning Stochastic Feedforward Networks
by Neal et al., Technical Report 1990

A Practical Bayesian Framework for Backprop Networks
by MacKay et al., Neural Computation 1992

Keeping Neural Networks Simple by Minimizing the Description Length of the Weights
by Hinton et al., COLT 1993

Practical Variational Inference for Neural Networks
by Alex Graves, NIPS 2011

Auto-Encoding Variational Bayes
by Kingma et al., ArXiv 2014

Deep Exponential Families
by Ranganath et al., AISTATS 2015

Weight Uncertainty in Neural Networks
by Blundell et al., ICML 2015

Probabilistic Backpropagation for ScalableLearning of Bayesian Neural Networks
by Hernandez-Lobato et al., ICML 2015

Variational Dropout and the Local Reparameterization Trick
by Kingma et al., NIPS 2015

The Poisson Gamma Belief Network
by Zhou et al., NIPS 2015

Deep Poisson Factor Modeling
by Henao et al., NIPS 2015

Natural-Parameter Networks: A Class of Probabilistic Neural Networks
by Wang et al., NIPS 2016
[PDF] [Project Page] [Code]

Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
by Mescheder et al., ICML 2017

Stick-Breaking Variational Autoencoders
by Nalisnick et al., ICLR 2017

Bayesian GAN
by Saatchi et al, NIPS 2017

Lightweight Probabilistic Deep Networks
by Gast et al., CVPR 2018

Feed-forward Propagation in Probabilistic Neural Networks with Categorical and Max Layers
by Shekhovtsov et al., ICLR 2018

ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees
by He et al., ICLR 2019
[PDF] [Project Page]

Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation
by Postels et al., ICCV 2019

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