Hierarchical Variational Models

7 November 2015

Papers citing "Hierarchical Variational Models"

50 / 78 papers shown

Title
A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation M. Valiuddin R. V. Sloun C.G.A. Viviers Peter H. N. de With Fons van der Sommen UQCV 91 1 0 25 Nov 2024
ELBOing Stein: Variational Bayes with Stein Mixture Inference Ola Rønning Eric T. Nalisnick Christophe Ley Padhraic Smyth Thomas Hamelryck BDL 52 1 0 30 Oct 2024
Provably Scalable Black-Box Variational Inference with Structured Variational Families Joohwan Ko Kyurae Kim W. Kim Jacob R. Gardner BDL 33 2 0 19 Jan 2024
Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors Wasu Top Piriyakulkij Yingheng Wang Volodymyr Kuleshov DiffM 40 1 0 05 Jan 2024
Learning Energy-Based Prior Model with Diffusion-Amortized MCMC Peiyu Yu Y. Zhu Sirui Xie Xiaojian Ma Ruiqi Gao Song-Chun Zhu Ying Nian Wu DiffM 29 12 0 05 Oct 2023
Parameter Estimation in DAGs from Incomplete Data via Optimal Transport Vy Vo Trung Le L. Vuong He Zhao Edwin V. Bonilla Dinh Q. Phung OT 21 4 0 25 May 2023
Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions Raghav Singhal Mark Goldstein Rajesh Ranganath DiffM 33 21 0 14 Feb 2023
Predictive World Models from Real-World Partial Observations Robin Karlsson Alexander Carballo Keisuke Fujii Kento Ohtani K. Takeda 35 5 0 12 Jan 2023
Constraining cosmological parameters from N-body simulations with Variational Bayesian Neural Networks Héctor J. Hortúa L. '. García Leonardo Castañeda C. BDL 24 4 0 09 Jan 2023
$β$ -Multivariational Autoencoder for Entangled Representation Learning in Video Frames F. Nouri R. Bergevin 16 0 0 22 Nov 2022
GFlowNets and variational inference Nikolay Malkin Salem Lahlou T. Deleu Xu Ji J. E. Hu Katie Everett Dinghuai Zhang Yoshua Bengio BDL 134 77 0 02 Oct 2022
Unifying Generative Models with GFlowNets and Beyond Dinghuai Zhang Ricky T. Q. Chen Nikolay Malkin Yoshua Bengio BDL AI4CE 54 25 0 06 Sep 2022
Latent Variable Modelling Using Variational Autoencoders: A survey Vasanth Kalingeri CML DRL 26 2 0 20 Jun 2022
MixFlows: principled variational inference via mixed flows Zuheng Xu Na Chen Trevor Campbell 55 8 0 16 May 2022
Efficient-VDVAE: Less is more Louay Hazami Rayhane Mama Ragavan Thurairatnam BDL 26 28 0 25 Mar 2022
Alleviating Adversarial Attacks on Variational Autoencoders with MCMC Anna Kuzina Max Welling Jakub M. Tomczak AAML DRL 31 12 0 18 Mar 2022
Multi-Task Neural Processes Jiayi Shen Xiantong Zhen M. Worring Ling Shao BDL 21 8 0 10 Nov 2021
Probabilistic Autoencoder using Fisher Information J. Zacherl Philipp Frank T. Ensslin EgoV AI4CE 33 2 0 28 Oct 2021
Relay Variational Inference: A Method for Accelerated Encoderless VI Amir Zadeh Santiago Benoit Louis-Philippe Morency DRL 17 1 0 26 Oct 2021
Variational Bayesian Approximation of Inverse Problems using Sparse Precision Matrices Jan Povala Ieva Kazlauskaite Eky Febrianto F. Cirak Mark Girolami 32 22 0 22 Oct 2021
Variational Predictive Routing with Nested Subjective Timescales Alexey Zakharov Qinghai Guo Z. Fountas BDL AI4TS 43 9 0 21 Oct 2021
On Incorporating Inductive Biases into VAEs Ning Miao Emile Mathieu N. Siddharth Yee Whye Teh Tom Rainforth CML DRL 30 10 0 25 Jun 2021
Black Box Variational Bayesian Model Averaging Vojtech Kejzlar Shrijita Bhattacharya Mookyong Son T. Maiti BDL 24 3 0 23 Jun 2021
ADAVI: Automatic Dual Amortized Variational Inference Applied To Pyramidal Bayesian Models Louis Rouillard Demian Wassermann 36 2 0 23 Jun 2021
Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks Anna Kuzina Max Welling Jakub M. Tomczak AAML DRL 36 10 0 10 Mar 2021
Efficient Semi-Implicit Variational Inference Vincent Moens Hang Ren A. Maraval Rasul Tutunov Jun Wang H. Ammar 85 6 0 15 Jan 2021
Planning from Pixels in Atari with Learned Symbolic Representations Andrea Dittadi Frederik K. Drachmann Thomas Bolander 26 11 0 16 Dec 2020
Unsupervised Learning of Global Factors in Deep Generative Models I. Peis Pablo Martínez Olmos Antonio Artés-Rodríguez BDL DRL 29 8 0 15 Dec 2020
Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks Thomas Bird F. Kingma David Barber SyDa MQ AI4CE 26 9 0 26 Oct 2020
Uncertainty in Neural Processes Saeid Naderiparizi Ke-Li Chiu Benjamin Bloem-Reddy Frank Wood UQCV BDL AI4CE 11 4 0 08 Oct 2020
Variational Inference with Continuously-Indexed Normalizing Flows Anthony L. Caterini R. Cornish Dino Sejdinovic Arnaud Doucet BDL 24 19 0 10 Jul 2020
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors Yuling Yao Aki Vehtari Andrew Gelman 29 60 0 22 Jun 2020
Capturing Label Characteristics in VAEs Thomas Joy Sebastian M. Schmon Philip Torr N. Siddharth Tom Rainforth CML DRL 30 43 0 17 Jun 2020
An Overview of Deep Semi-Supervised Learning Yassine Ouali C´eline Hudelot Myriam Tami SSL HAI 27 294 0 09 Jun 2020
Infinite-dimensional gradient-based descent for alpha-divergence minimisation Kamélia Daudel Randal Douc Franccois Portier 21 17 0 20 May 2020
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes Sebastian W. Ober Laurence Aitchison BDL 26 60 0 17 May 2020
Variational Inference with Vine Copulas: An efficient Approach for Bayesian Computer Model Calibration Vojtech Kejzlar T. Maiti 16 6 0 28 Mar 2020
Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models Chin-Wei Huang Laurent Dinh Aaron Courville DRL 31 87 0 17 Feb 2020
Thompson Sampling via Local Uncertainty Zhendong Wang Mingyuan Zhou 16 19 0 30 Oct 2019
Balancing Reconstruction Quality and Regularisation in ELBO for VAEs Shuyu Lin Stephen J. Roberts Niki Trigoni R. Clark DRL 21 15 0 09 Sep 2019
General Control Functions for Causal Effect Estimation from Instrumental Variables A. Puli Rajesh Ranganath CML 21 4 0 08 Jul 2019
Quality of Uncertainty Quantification for Bayesian Neural Network Inference Jiayu Yao Weiwei Pan S. Ghosh Finale Doshi-Velez UQCV BDL 24 113 0 24 Jun 2019
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings P. Elinas Edwin V. Bonilla Louis C. Tiao BDL GNN 26 10 0 05 Jun 2019
Discrete Flows: Invertible Generative Models of Discrete Data Dustin Tran Keyon Vafa Kumar Krishna Agrawal Laurent Dinh Ben Poole DRL 24 114 0 24 May 2019
Ensemble Model Patching: A Parameter-Efficient Variational Bayesian Neural Network Oscar Chang Yuling Yao David Williams-King Hod Lipson BDL UQCV 32 8 0 23 May 2019
Improved Conditional VRNNs for Video Prediction Lluis Castrejon Nicolas Ballas Aaron Courville VGen DRL 21 161 0 27 Apr 2019
Bayesian Adversarial Spheres: Bayesian Inference and Adversarial Examples in a Noiseless Setting Artur Bekasov Iain Murray AAML BDL 20 14 0 29 Nov 2018
Partitioned Variational Inference: A unified framework encompassing federated and continual learning T. Bui Cuong V Nguyen S. Swaroop Richard Turner FedML 24 55 0 27 Nov 2018
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE Chao Ma Sebastian Tschiatschek Konstantina Palla José Miguel Hernández-Lobato Sebastian Nowozin Cheng Zhang 16 127 0 28 Sep 2018
A Review of Learning with Deep Generative Models from Perspective of Graphical Modeling Zhijian Ou 31 16 0 05 Aug 2018