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An Updating Survey for Bayesian Deep Learning (BDL)

Survey

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
[PDF]

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

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

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

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

BDL and Healthcare

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

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

Black Box FDR
by Tansey et al., ICML 2018
[PDF]

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

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

BDL and NLP

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

BDL and Computer Vision

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

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

BDL and Control

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

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
[PDF]

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

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

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

BDL and Topic Modeling

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

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

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

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

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

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

BDL and Speech Recognition

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

BDL as a Framework (Miscellaneous)

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

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

Bayesian/Probabilistic Neural Networks as Building Blocks of BDL

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

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

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

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

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

Deep Exponential Families
by Ranganath et al., AISTATS 2015
[PDF]

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

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

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

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

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

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
[PDF]

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

Bayesian GAN
by Saatchi et al, NIPS 2017
[PDF]

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

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

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
[PDF]

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