Authors
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Title
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Mahdi Azarafrooz
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Doubly Stochastic Adversarial Autoencoder [paper]
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Ashwin D'Cruz, Sebastian Nowozin and Bill Byrne
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Tradeoffs in Neural Variational Inference [paper]
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Samuel L. Smith and Quoc V. Le
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A Bayesian Perspective on Generalization and Stochastic Gradient Descent [paper]
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Chi Zhang, Jiasheng Tang, Hao Li, Cheng Yang, Shenghuo Zhu and Rong Jin
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An Asynchronous Variance Reduced Framework for Efficient Bayesian Deep Learning [paper]
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Hengyuan Hu and Ruslan Salakhutdinov
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Learning Deep Generative Models With Discrete Latent Variables [paper]
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Kira Kempinska and John Shawe-Taylor
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Adversarial Sequential Monte Carlo [paper]
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Yunhao Tang and Alp Kucukelbir
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Variational Deep Q Network [paper]
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Hanna Tseran and Tatsuya Harada
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Memory Augmented Neural Network with Gaussian Embeddings for One-Shot Learning [paper]
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Joseph Marino, Yisong Yue and Stephan Mandt
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Iterative Inference Models [paper]
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Matthias Bauer, Mateo Rojas-Carulla, Jakub Swiatkowski, Bernhard Schoelkopf and Richard E. Turner
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Discriminative k-shot learning using probabilistic models [paper]
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Arya Pourzanjani, Richard Jiang and Linda Petzold
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Improving the Identifiability of Neural Networks for Bayesian Inference [paper]
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Marco Federici, Karen Ullrich and Max Welling
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Improved Bayesian Compression [paper]
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Maja Rudolph, Francisco Ruiz and David Blei
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Word2Net: Deep Representations of Language [paper]
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Raanan Yehezkel Rohekar, Guy Koren, Shami Nisimov and Gal Novik
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Unsupervised Deep Structure Learning by Recursive Independence Testing [paper]
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Eric Zhan, Stephan Zheng and Yisong Yue
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MAGnet: Generating Long-Term Multi-Agent Trajectories [paper]
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Dimity Miller, Lachlan Nicholson, Feras Dayoub and Niko Sünderhauf
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Dropout Variational Inference Improves Object Detection in Open-Set Conditions [paper]
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Patrick Mcclure and Nikolaus Kriegeskorte
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Robustly representing uncertainty through sampling in deep neural networks [paper]
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Ian Dewancker, Jakob Bauer and Michael McCourt
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Sequential Preference-Based Optimization [paper]
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Ryan Turner and Brady Neal
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How well does your sampler really work? [paper]
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Kimin Lee, Honglak Lee, Kibok Lee and Jinwoo Shin
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Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples [paper]
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Alexander Amini, Ava Soleimany, Sertac Karaman and Daniela Rus
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Spatial Uncertainty Sampling for End to End Control [paper]
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Bin Liu, Lirong He, Shandian Zhe, Yingming Li and Zenglin Xu
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DeepCP: Nonlinear Tensor Decomposition as a Deep Generative Model [paper]
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Cuong Nguyen, Yingzhen Li, Thang Bui and Richard Turner
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Variational Continual Learning in Deep Models [paper]
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Stefan Webb, Adam Golinski, Robert Zinkov and Frank Wood
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Principled Inference Networks in Deep Generative Models [paper]
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Subhadip Mukherjee, Debabrata Mahapatra and Chandra Sekhar Seelamantula
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DNNs for sparse coding and dictionary learning [paper]
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Onur Ozdemir, Benjamin Woodward and Andrew Berlin
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Propagating Uncertainty in Multi-Stage Bayesian Convolutional Neural Networks with Application to Pulmonary Nodule Detection [paper]
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Jonathan Gordon and Jose Miguel Hernandez-Lobato
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Bayesian Semisupervised Learning with Deep Generative Models [paper]
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David Krueger, Chin-Wei Huang, Riashat Islam, Ryan Turner, Alexandre Lacoste and Aaron Courville
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Bayesian Hypernetworks [paper]
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Guodong Zhang, Shengyang Sun and Roger Grosse
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Natural Gradient as Stochastic Variational Inference [paper]
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Nick Pawlowski, Martin Rajchl and Ben Glocker
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Implicit Weight Uncertainty in Neural Networks [paper]
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Ambrish Rawat, Martin Wistuba and Maria-Irina Nicolae
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Harnessing Model Uncertainty for Detecting Adversarial Examples [paper]
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Xun Zheng, Manzil Zaheer, Amr Ahmed, Yuan Wang, Eric Xing and Alex Smola
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Particle MCMC for Latent LSTM Allocation [paper]
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Yingzhen Li
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Approximate Gradient Decent for Training Implicit Generative Models [paper]
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Aleksander Wieczorek, Mario Wieser, Damian Murezzan and Volker Roth
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Deep Copula Information Bottleneck [paper]
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Chin-Wei Huang and Aaron Courville
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Sequentialized Sampling Importance Resampling and Scalable IWAE [paper]
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Soumya Ghosh and Finale Doshi-Velez
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Model Selection in Bayesian Neural Networks via Horseshoe Priors [paper]
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Sergey Tulyakov, Andrew Fitzgibbon and Sebastian Nowozin
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Hybrid VAE: Improving Deep Generative Models using Partial Observations [paper]
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Hippolyt Ritter, Aleksandar Botev and David Barber
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A Scalable Laplace Approximation for Neural Networks [paper]
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Jiri Hron, Alexander Matthews and Zoubin Ghahramani
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Two Problems with Variational Gaussian Dropout [paper]
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Aritra Bhowmik, Aniruddha Adiga, Chandra Sekhar Seelamantula, Fabian Hauser, Jaroslaw Jacak and Bettina Heise
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Bayesian Deep Deconvolutional Neural Networks [paper]
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Christopher Tegho, Pawel Budzianowski and Milica Gasic
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Uncertainty Estimates for Efficient Neural Network-based Dialogue Policy Optimisation [paper]
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Abdul-Saboor Sheikh, Kashif Rasul, Andreas Merentitis and Urs Bergmann
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Stochastic Maximum Likelihood Optimization via Hypernetworks [paper]
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Hugh Salimbeni and Marc Deisenroth
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Deeply Non-Stationary Gaussian Processes [paper]
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Mohammad Emtiyaz Khan, Zuozhu Liu, Voot Tangkaratt and Yarin Gal
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Vprop: Variational Inference using RMSprop [paper]
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Leonard Hasenclever, Jakub Tomczak, Rianne van den Berg and Max Welling
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Variational Inference with Orthogonal Normalizing Flow [paper]
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Alex Lewandowski
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Batch Normalized Deep Kernel Learning for Weight Uncertainty [paper]
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Gintare Karolina Dzuigaite and Daniel Roy
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Entropy-SG(L)D Optimizes the Prior of a (Valid) PAC-Bayes Bound [paper]
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Casper Kaae Sønderby, Ben Poole and Andriy Mnih
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Continuous Relaxation Training of Discrete Latent Variable Image Models [paper]
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Tom Rainforth, Tuan Anh Le, Maximilian Igl, Chris J. Maddison and Frank Wood
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Tighter ELBOs are Not Necessarily Better [paper]
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Jhosimar Arias Figueroa and Adín Ramírez Rivera
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Is Simple Better?: Revisiting Simple Generative Models for Unsupervised Clustering [paper]
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Carlos Riquelme, George Tucker and Jasper Snoek
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Deep Bayesian Bandits Showdown [paper]
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Dustin Tran, Yura Burda and Ilya Sutskever
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Generative Models for Alignment and Data Efficiency in Language [paper]
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Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S. Schoenholz, Jeffrey Pennington and Jascha Sohl-Dickstein
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Deep Neural Networks as Gaussian Processes [paper]
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Shengjia Zhao, Jiaming Song and Stefano Ermon
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A Lagrangian Perspective on Latent Variable Generative Modeling [paper]
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Alexandre Lacoste, Thomas Boquet, Negar Rostamzadeh, Boris Oreshki, Wonchang Chung and David Krueger
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Deep Prior [paper]
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John Lambert, Ozan Sener and Silvio Savarese
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Deep Learning under Privileged Information [paper]
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Rui Shu, Hung H. Bui and Stefano Ermon
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AC-GAN Learns a Biased Distribution [paper]
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Daniel Flam-Shepherd, James Requeima and David Duvenaud
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Mapping Gaussian Process Priors to Bayesian Neural Networks [paper]
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Matthew Hoffman, Carlos Riquelme and Matthew Johnson
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The Beta-VAE's Implicit Prior [paper]
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Juan Camilo Gamboa Higuera, David Meger and Gregory Dudek
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Synthesizing Neural Network Controllers with Probabilistic Model-Based Reinforcement Learning [paper]
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Tim G. J. Rudner and Dino Sejdinovic
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Inter-domain Deep Gaussian Processes [paper]
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Brian Trippe and Richard Turner
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Conditional Density Estimation with Bayesian Normalizing Flows [paper]
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Stanislav Fort
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Gaussian Prototypical Networks for Few-Shot Learning on Omniglot [paper]
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Aditya Grover, Aaron Zweig and Stefano Ermon
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Graphite: Iterative Generative Modeling of Graphs [paper]
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Peter Henderson, Thang Doan, Riashat Islam and David Meger
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Bayesian Policy Gradients via Alpha Divergence Dropout Inference [paper]
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Maxime Voisin and Daniel Ritchie
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An Improved Training Procedure for Neural Autoregressive Data Completion [paper]
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Chin-Wei Huang, David Krueger and Aaron Courville
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Facilitating Multimodality in Normalizing Flows [paper]
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Luigi Malagò, Alexandra Peste and Septimia Sarbu
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An Explanatory Analysis of the Geometry of Latent Variables Learned by Variational Auto-Encoders [paper]
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