Bayesian Deep Learning Workshop

Schedule & Accepted Papers

Slides for invited talks are now online (see [slides] below), as well as workshop video recordings.

Confirmed Speakers

Debora Marks
(Harvard Medical School / Broad Institute)

Chelsea Finn
(Google Brain / Berkeley / Stanford)

Yingzhen Li
(Microsoft Research)

Alexander G. de G. Matthews
(DeepMind)

Schedule

8.00 - 8.05	Opening remarks
8.05 - 8.25	Invited talk	Alexander G. de G. Matthews (DeepMind) [slides]	Gaussian Process Behaviour in Wide Deep Neural Networks
8.25 - 8.40	Contributed talk	Tim G. J. Rudner, Florian Wenzel and Yarin Gal	The Natural Neural Tangent Kernel: Neural Network Training Dynamics under Natural Gradient Descent
8.40 - 9.00	Invited talk	Yingzhen Li (Microsoft Research) [slides]	On estimating epistemic uncertainty (tentative)
9.00 - 9.15	Contributed talk	Stanislav Fort, Huiyi Hu and Balaji Lakshminarayanan	Deep Ensembles: A Loss Landscape Perspective
9.15 - 9.30	Poster spotlights
9.30 - 10.30	Discussion over coffee and poster session
10.30 - 10.50	Invited talk	Andrew Gordon Wilson (NYU) [slides]	Using Loss Surface Geometry for Scalable Bayesian Deep Learning
10.50 - 11.05	Contributed talk	Abhishek Kumar and Ben Poole	On Implicit Regularization in β-VAE
11.05 - 11.25	Invited talk	Jasper Snoek (Google) [slides]	Uncertainty under distributional shift
11.25 - 11.40	Contributed talk	Sicong Huang, Alireza Makhzani, Yanshuai Cao and Roger Grosse	Evaluating Lossy Compression Rates of Deep Generative Models
11.40 - 13.20	Lunch
13.20 - 13.40	Invited talk	Chelsea Finn (Google Brain / Berkeley / Stanford) [slides]	The Big Problem with Meta-Learning and How Bayesians Can Fix It
13.40 - 13.55	Contributed talk	Riccardo Moriconi, Marc Peter Deisenroth and Senanayak Sesh Kumar Karri	High-dimensional Bayesian optimization using low-dimensional feature spaces
13.55 - 14.15	Invited talk	Roger Grosse (Toronto) [slides]	Functional variational Bayesian neural networks
14.15 - 14.30	Contributed talk	Sebastian Farquhar, Lewis Smith and Yarin Gal	Try Depth Instead of Weight Correlations: Mean-field is a Less Restrictive Assumption for Variational Inference in Deep Networks
14.30 - 15.30	Discussion over coffee and poster session
15.30 - 15.45	Contributed talk	Andrew Ross, Jianzhun Du, Yonadav Shavit and Finale Doshi-Velez	Controlled Direct Effect Priors for Bayesian Neural Networks
15.45 - 16.05	Invited talk	Debora Marks (Harvard Medical School / Broad Institute) [slides]	Deep generative models for genetic variation and drug design
16.05 - 16.20	Contributed talk	Mihaela Rosca, Michael Figurnov, Shakir Mohamed and Andriy Mnih	Measure Valued Derivatives for Approximate Bayesian Inference
16.20 - 17.30	Panel Session	Panellists: Debora Marks Jasper Snoek Chelsea Finn Andrew Gordon Wilson Yingzhen Li Roger Grosse Alexander G. de G. Matthews Moderator: Finale Doshi-Velez
17.30 - 19.30	Poster session

Accepted Abstracts

We added all camera ready submissions sent to us by 11/12/2019. If a paper is not online, please contact the lead author and encourage them to send us the camera ready.

Authors	Title
Andrew Y. K. Foong, David R. Burt, Yingzhen Li and Richard E. Turner	Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks [paper]
Paulo Rauber, Aditya Ramesh and Jürgen Schmidhuber	Recurrent neural-linear posterior sampling for non-stationary bandits [paper]
Andreas Look and Melih Kandemir	Differential Bayesian Neural Nets [paper]
Matt Benatan and Edward Pyzer-Knapp	Fully Bayesian Recurrent Neural Networks for Safe Reinforcement Learning [paper]
Ruiyi Zhang, Changyou Chen, Zhe Gan, Zheng Wen and Lawrence Carin	Nested-Wasserstein Self-Imitation Learning for Sequence Generation [paper]
Marton Havasi, Jasper Snoek, Dustin Tran, Jonathan Gordon and Jose Miguel Hernandez-Lobato	Refining the variational posterior through iterative optimization [paper]
Hlynur Jonsson, Giovanni Cherubini and Evangelos Eleftheriou	Convergence of DNNs with mutual-information-based regularization [paper]
Fredrik K. Gustafsson, Martin Danelljan and Thomas B. Schön	Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision [paper]
Angelos Filos, Sebastian Farquhar, Aidan N. Gomez, Tim G. J. Rudner, Zachary Kenton, Lewis Smith, Milad Alizadeh, Arnoud de Kroon and Yarin Gal	A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks [paper]
James Brofos, Rui Shu and Roy Lederman	A Bias-Variance Decomposition for Bayesian Deep Learning [paper]
Nabeel Seedat and Christopher Kanan	Towards calibrated and scalable uncertainty representations for neural networks [paper]
Otmane Sakhi, Stephen Bonner, David Rohde and Flavian Vasile	Reconsidering analytical variational bounds for output layers of deep networks [paper]
Micha Livne, Kevin Swersky and David Fleet	High Mutual Information in Representation Learning with Symmetric Variational Inference [paper]
Hao Fu, Chunyuan Li, Ke Bai, Jianfeng Gao and Lawrence Carin	Flexible Text Modeling withSemi-Implicit Latent Representations [paper]
Suwen Lin, Martin Wistuba, Ambrish Rawat and Nitesh Chawla	Neural Tree Kernel Learning [paper]
Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser, Erik Bodin, Carl Henrik Ek and Neill Campbell	Compositional uncertainty in deep Gaussian processes [paper]
Adam D Cobb, Atilim Günes Baydin, Ivan Kiskin, Andrew Markham and Stephen Roberts	Semi-separable Hamiltonian Monte Carlo for inference in Bayesian neural networks [paper]
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh and Balaji Lakshminarayanan	Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality [paper]
Felix McGregor, Arnu Pretorius, Johan du Preez and Steve Kroon	Stabilising priors for robust Bayesian deep learning [paper]
John Moberg, Lennart Svensson, Juliano Pinto and Henk Wymeersch	Bayesian Linear Regression on Deep Representations [paper]
Bang An, Xuannan Dong and Changyou Chen	Repulsive Bayesian Sampling for Diversified Attention Modeling [paper]
Colin White, Willie Neiswanger and Yash Savani	Deep Uncertainty Estimation for Model-based Neural Architecture Search [paper]
Vincent Fortuin and Gunnar Rätsch	Deep Mean Functions for Meta-Learning in Gaussian Processes [paper]
Anirudh Suresh and Srivatsan Srinivasan	Improved Attentive Neural Processes [paper]
Patrick McClure, Nao Rho, John Lee, Jakub Kaczmarzyk, Charles Zheng, Satrajit Ghosh, Dylan Nielson, Adam Thomas, Peter Bandettini and Francisco Pereira	Improving 3D Brain Segmentation using a Spike-and-Slab Bayesian Deep Neural Network [paper]
Tim R. Davidson, Jakub M. Tomczak and Efstratios Gavves	Increasing Expressivity of a Hyperspherical VAE [paper]
Gaurush Hiranandani, Sumeet Katariya, Nikhil Rao and Karthik Subbian	Online Bayesian Learning for E-Commerce Query Reformulation [paper]
Sanjeev Arora, Simon Du, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang and Dingli Yu	On the Power of NTK on Small Data [paper]
Prithvijit Chakrabarty and Subhransu Maji	The Spectral Bias of the Deep Image Prior [paper]
Masha Itkina, Boris Ivanovic, Ransalu Senanayake, Mykel Kochenderfer and Marco Pavone	Evidential Disambiguation of Latent Multimodality in Conditional Variational Autoencoders [paper]
Simone Rossi, Sébastien Marmin and Maurizio Filippone	Efficient Approximate Inference with Walsh-Hadamard Variational Inference [paper]
Didrik Nielsenand Ole Winther	PixelCNN as a Single-Layer Flow [paper]
Changyong Oh, Kamil Adamczewski and Mijung Park	The Radial and Directional Posteriors for Bayesian Deep Learning [paper]
William Harvey, Michael Teng and Frank Wood	Near-Optimal Glimpse Sequences for Training Hard Attention Neural Networks [paper]
Matias Valdenegro-Toro	Deep Sub-Ensembles for Fast Uncertainty Estimation in Image Classification [paper]
Stefano Peluchetti and Stefano Favaro	Neural SDE - Information propagation through the lens of diffusion processes [paper]
Riccardo Moriconi, Marc Peter Deisenroth and Senanayak Sesh Kumar Karri	High-dimensional Bayesian optimization using low-dimensional feature spaces [paper]
Ali Hebbal, Loic Brevault, Mathieu Balesdent, El-Ghazali Talbi and Nouredine Melab	Multi-fidelity modeling using DGPs: Improvements and a generalization to varying input space dimensions [paper]
Wen Yao, Jun Zhang, Qiang Chang, Xiaozhou Zhu and Weien Zhou	Error Estimation of Sampling-free Uncertainty Propagation in Bayesian Neural Network with Simplified Covariance Matrix [paper]
Sebastian Farquhar, Lewis Smith and Yarin Gal	Try Depth Instead of Weight Correlations: Mean-field is a Less Restrictive Assumption for Variational Inference in Deep Networks [paper]
Tianyu Cui, Pekka Marttinen and Samuel Kaski	Learning Global Pairwise Interactions with Bayesian Neural Networks [paper]
Da Tang, Dawen Liang, Nicholas Ruozzi and Tony Jebara	Learning Correlated Latent Representations with Adaptive Priors [paper]
Vaclav Smidl, Jan Bim and Tomas Pevny	Orthogonal Approximation of Marginal Likelihood of Generative Models [paper]
Augustin Prado, Ravinath Kausik and Lalitha Venkataramanan	Dual Neural Network Architecture for Determining Epistemic and Aleatoric Uncertainties [paper]
Chunlin Ji and Haige Shen	Stochastic Variational Inference via Upper Bound [paper]
Taylan Cemgil, Sumedh Ghaisas, Krishnamurthy Dvijotham and Pushmeet Kohli	Learning Perturbation-Invariant Representations with Smooth Encoders [paper]
Jack Fitzsimons, Sebastian Schmon and Stephen Roberts	Implicit Priors for Knowledge Sharing in Bayesian Neural Networks [paper]
Mike Wu, Kristy Choi, Noah Goodman and Stefano Ermon	Meta-Amortized Variational Inference and Learning [paper]
He Zhao, Piyush Rai, Lan Du, Wray Buntine, Dinh Phung and Mingyuan Zhou	A Bayesian Extension to VAEs for Discrete Data [paper]
Agustinus Kristiadi, Sina Däubener and Asja Fischer	Uncertainty quantification with compound density networks [paper]
Niklas Heim, Václav Šmídl and Tomáš Pevný	Rodent: Relevance determination in ODE [paper]
Kathrin Grosse, David Pfaff, Michael T. Smith and Michael Backes	The Limitations of Model Uncertainty in Adversarial Settings [paper]
Timon Willi, Jonathan Masci, Jürgen Schmidhuber and Christian Osendorfer	Recurrent Neural Processes [paper]
Samuel Kessler, Vu Nguyen, Stefan Zohren and Steve Roberts	Indian Buffet Neural Networks for Continual Learning [paper]
Mariana Vargas Vieyra, Aurélien Bellet and Pascal Denis	Probabilistic End-to-End Graph-based Semi-Supervised Learning [paper]
Apratim Bhattacharyya, Mario Fritz and Bernt Schiele	“Best of Many” Samples Distribution Matching [paper]
Apratim Bhattacharyya, Michael Hanselmann, Mario Fritz, Bernt Schiele and Christoph-Nikolas Straehle	Conditional Flow Variational Autoencoders for Structured Sequence Prediction [paper]
Rahul Sharma, Abhishek Kumar and Piyush Rai	Refined $\alpha$-Divergence Variational Inference via Rejection Sampling [paper]
Luis A. Perez Rey, Vlado Menkovski and Jacobus W. Portegies	Can VAEs capture topological properties? [paper]
Geoffroy Dubourg-Felonneau, Omar Darwish, Christopher Parsons, Dàmi Rebergen, John Cassidy, Nirmesh Patel and Harry Clifford	Deep Bayesian Recurrent Neural Networks for Somatic Variant Calling in Cancer [paper]
Jonathan Warrell and Mark Gerstein	Hierarchical PAC-Bayes Bounds via Deep Probabilistic Programming [paper]
Slava Voloshynovskiy, Mouad Kondah, Shideh Rezaeifar, Olga Taran, Taras Holotyak and Danilo J. Rezende	Information bottleneck through variational glasses [paper]
Mihaela Rosca, Michael Figurnov, Shakir Mohamed and Andriy Mnih	Measure Valued Derivatives for Approximate Bayesian Inference [paper]
Max-Heinrich Laves, Sontje Ihler, Karl-Philipp Kortmann and Tobias Ortmaier	Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference [paper]
Matthew Willetts, Stephen Roberts and Chris Holmes	Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders [paper]
Jiaming Song and Stefano Ermon	Mutual Information Estimation as Optimization over Density Ratios: A Unifying Perspective [paper]
Andrew Ross, Jianzhun Du, Yonadav Shavit and Finale Doshi-Velez	Controlled Direct Effect Priors for Bayesian Neural Networks [paper]
Yeming Wen, Dustin Tran and Jimmy Ba	BatchEnsemble: Efficient Ensemble of Deep Neural Networks via Rank-1 Perturbation [paper]
Homa Fashandi and Darin Graham	Empirical Studies on Sensitivity to Perturbations and Hyperparameters in Bayesian Neural Networks [paper]
Ruiqi Gao, Erik Nijkamp, Zhen Xu, Andrew Dai, Diederik Kingma and Ying Nian Wu	Flow Contrastive Estimation of Energy-Based Model [paper]
Hooshmand Shokri Razaghi and Liam Paninski	Filtering Normalizing Flows [paper]
Joshua Chang, Shashaank Vattikuti and Carson Chow	Probabilistically-autoencoded horseshoe-disentangled multidomain item-response theory models [paper]
Tim Xiao, Aidan Gomez and Yarin Gal	Wat heb je gezegd? Detecting Out-of-Distribution Translations with Variational Transformers [paper]
Vanessa Boehm, Francois Lanusse and Uros Seljak	Uncertainty Quantification with Generative Models [paper]
Giorgio Giannone, Christian Osendorfer and Jonathan Masci	No Representation without Transformation [paper]
Gintare Karolina Dziugaite, Waseem Gharbieh, Kyle Hsu and Daniel Roy	Optimal (PAC-Bayes) priors are data dependent [paper]
Ranganath Krishnan, Mahesh Subedar, Omesh Tickoo, Angelos Filos and Yarin Gal	Improving MFVI in Bayesian Neural Networks with Empirical Bayes: a Study with Diabetic Retinopathy Diagnosis [paper]
Ari Heljakka, Yuxin Hou, Juho Kannala and Arno Solin	Conditional Image Sampling by Deep Automodulators [paper]
Joe Davison, Kristen Severson and Soumya Ghosh	Cross-population Variational Autoencoders [paper]
Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov and Dmitry Vetrov	Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning [paper]
Jeremiah Liu	Variable Selection with Rigorous Uncertainty Quantification using Bayesian Deep Neural Networks [paper]
Sicong Huang, Alireza Makhzani, Yanshuai Cao and Roger Grosse	Evaluating Lossy Compression Rates of Deep Generative Models [paper]
Dian Ang Yap, Nicholas Roberts and Vinay Prabhu	Grassmannian Packings in Neural Networks: Learning with Maximal Subspace Packings for Diversity and Anti-Sparsity [paper]
Evgenii Nikishin, Arsenii Ashukha and Dmitry Vetrov	Unsupervised Domain Adaptation with Shared Latent Dynamics for Reinforcement Learning [paper]
Imant Daunhawer, Thomas Sutter and Julia E. Vogt	Improving Multimodal Generative Models with Disentangled Latent Partitions [paper]
Sunho Park, Saehoon Kim, Hongming Xu and Tae Hyun Hwang	Deep Gaussian processes for weakly supervised learning: tumor mutation burden (TMB) prediction [paper]
Nilesh Ahuja, Ibrahima Ndiour, Trushant Kalyanpur and Omesh Tickoo	Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection [paper]
Stanislav Fort, Huiyi Hu and Balaji Lakshminarayanan	Deep Ensembles: A Loss Landscape Perspective [paper]
Abhishek Kumar and Ben Poole	On Implicit Regularization in β-VAE [paper]
Steindor Saemundsson, Katja Hofmann and Marc Deisenroth	Variational Integrator Networks [paper]
Mahesh Subedar, Nilesh Ahuja, Ranganath Krishnan, Ibrahima Ndiour and Omesh Tickoo	Deep Probabilistic Models to Detect Data Poisoning Attacks [paper]
Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Jascha Sohl-Dickstein and Samuel Schoenholz	Neural Tangents: Easy and Fast Infinite Networks in Python [paper]
Artyom Gadetsky, Kirill Struminsky, Christopher Robinson, Novi Quadrianto and Dmitry Vetrov	Low-variance Gradient Estimates for the Plackett-Luce Distribution [paper]
Jhosimar Arias Figueroa	Semi-supervised Learning using Deep Generative Models and Auxiliary Tasks [paper]
Erik Daxberger and José Miguel Hernández-Lobato	Bayesian VAEs for Unsupervised Anomaly Detection [paper]
Xavier Gitiaux, Shane Maloney, Anna Jungbluth, Carl Shneider, Atılım Güneş Baydin, Paul J. Wright, Yarin Gal, Michel Deudon, Alfredo Kalaitzis and Andres Munoz-Jaramillo	Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties [paper]
Tim G. J. Rudner, Florian Wenzel and Yarin Gal	The Natural Neural Tangent Kernel: Neural Network Training Dynamics under Natural Gradient Descent [paper]
Waseem Aslam, Tim G. J. Rudner and Yarin Gal	Tighter Variational Bounds for Deep Gaussian Processes [paper]
Adrián Csiszárik, Beatrix Benkő and Dániel Varga	Negative Sampling in Variational Autoencoders [paper]
Aditya Grover, Dustin Tran, Rui Shu, Ben Poole and Kevin Murphy	Probing Uncertainty Estimates of Neural Processes [paper]

Call for papers

We invite researchers to submit work in any of the following areas:

Uncertainty in deep learning,
probabilistic deep models (such as extensions and application of Bayesian neural networks),
deep probabilistic models (such as hierarchical Bayesian models and their applications),
deep generative models (such as variational autoencoders),
practical approximate inference techniques in Bayesian deep learning,
connections between deep learning and Gaussian processes,
applications of Bayesian deep learning,
or any of the topics below.

A submission should take the form of an extended abstract (3 pages long) in PDF format using the NeurIPS 2019 style. Author names do not need to be anonymized, and conflicts of interest in assessing submitted contributions will be based on this (reviewers will not be involved in the assessment of a submission by authors within the same institution). References may extend as far as needed beyond the 3 page upper limit. Submissions may extend beyond the 3 pages upper limit, but reviewers are not expected to read beyond the first 3 pages. If the research has previously appeared in a journal, workshop, or conference (including the NeurIPS 2019 conference), the workshop submission should extend that previous work. Dual submissions to ICLR 2019, AAAI 2019, and AISTATS 2019 are permitted.

Submissions will be accepted as contributed talks or poster presentations. Extended abstracts should be submitted by ~~September 9, 2019~~ Deadline has been extended to Friday, September 13, 2019; submission page is here. Final versions will be posted on the workshop website (and are archival but do not constitute a proceedings). Notification of acceptance will be made before October 1, 2019.

Key Dates:

Extended abstract submission deadline: ~~September 9, 2019 (23:59 AOE)~~ Deadline has been extended to Friday, September 13, 2019 (23:59 AOE) (submission page is here)
Acceptance notification: before October 1, 2019
Camera ready submission: December 1, 2019
Workshop: Friday, December 13, 2019

Please make sure to apply to the lottery registration as the workshop is allocated a limited number of tickets. We will do our best to guarantee workshop registration for all accepted workshop submissions which did not receive a lottery registration.

Abstract

While deep learning has been revolutionary for machine learning, most modern deep learning models cannot represent their uncertainty nor take advantage of the well studied tools of probability theory. This has started to change following recent developments of tools and techniques combining Bayesian approaches with deep learning. The intersection of the two fields has received great interest from the community over the past few years, with the introduction of new deep learning models that take advantage of Bayesian techniques, as well as Bayesian models that incorporate deep learning elements [1-11]. In fact, the use of Bayesian techniques in deep learning can be traced back to the 1990s’, in seminal works by Radford Neal [12], David MacKay [13], and Dayan et al. [14]. These gave us tools to reason about deep models’ confidence, and achieved state-of-the-art performance on many tasks. However earlier tools did not adapt when new needs arose (such as scalability to big data), and were consequently forgotten. Such ideas are now being revisited in light of new advances in the field, yielding many exciting new results.

Extending on last year’s workshop’s success, this workshop will again study the advantages and disadvantages of such ideas, and will be a platform to host the recent flourish of ideas using Bayesian approaches in deep learning and using deep learning tools in Bayesian modelling. The program includes a mix of invited talks, contributed talks, and contributed posters. It will be composed of five themes: deep generative models, variational inference using neural network recognition models, practical approximate inference techniques in Bayesian neural networks, applications of Bayesian neural networks, and information theory in deep learning. Future directions for the field will be debated in a panel discussion.

Previous workshops:

Our 2018 workshop page is available here; Our 2017 workshop page is available here; Our 2016 workshop page is available here; videos from the 2016 workshop are available online as well.

Topics

Uncertainty in deep learning,
Applications of Bayesian deep learning,
Probabilistic deep models (such as extensions and application of Bayesian neural networks),
Deep probabilistic models (such as hierarchical Bayesian models and their applications),
Generative deep models (such as variational autoencoders),
Information theory in deep learning,
Deep ensemble uncertainty,
NTK and Bayesian modelling,
Connections between NNs and GPs,
Incorporating explicit prior knowledge in deep learning (such as posterior regularisation with logic rules),
Approximate inference for Bayesian deep learning (such as variational Bayes / expectation propagation / etc. in Bayesian neural networks),
Scalable MCMC inference in Bayesian deep models,
Deep recognition models for variational inference (amortised inference),
Bayesian deep reinforcement learning,
Deep learning with small data,
Deep learning in Bayesian modelling,
Probabilistic semi-supervised learning techniques,
Active learning and Bayesian optimisation for experimental design,
Kernel methods in Bayesian deep learning,
Implicit inference,
Applying non-parametric methods, one-shot learning, and Bayesian deep learning in general.

Organisers

References

Kingma, DP and Welling, M, ‘’Auto-encoding variational bayes’’, 2013.
Rezende, D, Mohamed, S, and Wierstra, D, ‘’Stochastic backpropagation and approximate inference in deep generative models’’, 2014.
Blundell, C, Cornebise, J, Kavukcuoglu, K, and Wierstra, D, ‘’Weight uncertainty in neural network’’, 2015.
Hernandez-Lobato, JM and Adams, R, ’’Probabilistic backpropagation for scalable learning of Bayesian neural networks’’, 2015.
Gal, Y and Ghahramani, Z, ‘’Dropout as a Bayesian approximation: Representing model uncertainty in deep learning’’, 2015.
Gal, Y and Ghahramani, G, ‘’Bayesian convolutional neural networks with Bernoulli approximate variational inference’’, 2015.
Kingma, D, Salimans, T, and Welling, M. ‘’Variational dropout and the local reparameterization trick’’, 2015.
Balan, AK, Rathod, V, Murphy, KP, and Welling, M, ‘’Bayesian dark knowledge’’, 2015.
Louizos, C and Welling, M, “Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors”, 2016.
Lawrence, ND and Quinonero-Candela, J, “Local distance preservation in the GP-LVM through back constraints”, 2006.
Tran, D, Ranganath, R, and Blei, DM, “Variational Gaussian Process”, 2015.
Neal, R, ‘’Bayesian Learning for Neural Networks’’, 1996.
MacKay, D, ‘’A practical Bayesian framework for backpropagation networks‘’, 1992.
Dayan, P, Hinton, G, Neal, R, and Zemel, S, ‘’The Helmholtz machine’’, 1995.
Wilson, AG, Hu, Z, Salakhutdinov, R, and Xing, EP, “Deep Kernel Learning”, 2016.
Saatchi, Y and Wilson, AG, “Bayesian GAN”, 2017.
MacKay, D.J.C. “Bayesian Methods for Adaptive Models”, PhD thesis, 1992.

Bayesian Deep Learning

Schedule & Accepted Papers

Confirmed Speakers

Debora Marks
(Harvard Medical School / Broad Institute)

Jasper Snoek
(Google)

Chelsea Finn
(Google Brain / Berkeley / Stanford)

Andrew G Wilson
(NYU)

Roger Grosse
(U of Toronto)

Yingzhen Li
(Microsoft Research)

Alexander G. de G. Matthews
(DeepMind)

Schedule

Accepted Abstracts

Call for papers

Key Dates:

Abstract

Previous workshops:

Topics

Organisers

Yarin Gal
(Main Coordinator)

José Miguel
Hernández-Lobato
(External Communications)

Christos Louizos
(Programme Committee)

Eric Nalisnick
(Operations)

Zoubin Ghahramani

Kevin Murphy

Max Welling

References

Confirmed Speakers

Debora Marks (Harvard Medical School / Broad Institute)

Jasper Snoek (Google)

Chelsea Finn (Google Brain / Berkeley / Stanford)

Andrew G Wilson (NYU)

Roger Grosse (U of Toronto)

Yingzhen Li (Microsoft Research)

Alexander G. de G. Matthews (DeepMind)

Schedule

Accepted Abstracts

Key Dates:

Previous workshops:

Yarin Gal (Main Coordinator)

José Miguel Hernández-Lobato (External Communications)

Christos Louizos (Programme Committee)

Eric Nalisnick (Operations)

Zoubin Ghahramani

Kevin Murphy

Max Welling

Debora Marks
(Harvard Medical School / Broad Institute)

Jasper Snoek
(Google)

Chelsea Finn
(Google Brain / Berkeley / Stanford)

Andrew G Wilson
(NYU)

Roger Grosse
(U of Toronto)

Yingzhen Li
(Microsoft Research)

Alexander G. de G. Matthews
(DeepMind)

Yarin Gal
(Main Coordinator)

José Miguel
Hernández-Lobato
(External Communications)

Christos Louizos
(Programme Committee)

Eric Nalisnick
(Operations)