Deep Learning: A Bayesian Perspective

1 June 2017

Papers citing "Deep Learning: A Bayesian Perspective"

42 / 42 papers shown

Title
Accelerating Langevin Monte Carlo Sampling: A Large Deviations Analysis Nian Yao Pervez Ali Xihua Tao Lingjiong Zhu 51 0 0 24 Mar 2025
Kolmogorov GAM Networks are all you need! Sarah Polson Vadim Sokolov 39 0 0 03 Jan 2025
$Sparse Deep Learning Models with the $\ell_1$ Regularization$ Sparse Deep Learning Models with the $\ell_1$ Regularization Lixin Shen Rui Wang Yuesheng Xu Mingsong Yan 40 0 0 05 Aug 2024
Fusing Climate Data Products using a Spatially Varying Autoencoder Jacob A. Johnson Matthew J. Heaton William F. Christensen Lynsie R. Warr S. Rupper AI4CE 31 0 0 12 Mar 2024
Deep Neural Networks for Semiparametric Frailty Models via H-likelihood Hangbin Lee I. Ha Youngjo Lee 22 0 0 13 Jul 2023
Object Recognition in Different Lighting Conditions at Various Angles by Deep Learning Method Imran Khan Mirani Tianhua Chen Malak Abid Ali Khan Syed Muhammad Aamir Waseef Menhaj ObjD 19 8 0 18 Oct 2022
Batch Bayesian optimisation via density-ratio estimation with guarantees Rafael Oliveira Louis C. Tiao Fabio Ramos 47 7 0 22 Sep 2022
How to Combine Variational Bayesian Networks in Federated Learning Atahan Ozer Kadir Burak Buldu Abdullah Akgul Gözde B. Ünal FedML 38 5 0 22 Jun 2022
Bayesian Deep Learning with Multilevel Trace-class Neural Networks Neil K. Chada Ajay Jasra K. Law Sumeetpal S. Singh BDL UQCV 83 3 0 24 Mar 2022
Uncertainty-Aware Deep Multi-View Photometric Stereo Berk Kaya Suryansh Kumar C. Oliveira V. Ferrari Luc Van Gool 3DV 40 30 0 26 Feb 2022
Efficient Online Bayesian Inference for Neural Bandits Gerardo Duran-Martín Aleyna Kara Kevin Patrick Murphy BDL 32 13 0 01 Dec 2021
Low-rank variational Bayes correction to the Laplace method J. van Niekerk Haavard Rue BDL 26 13 0 25 Nov 2021
Merging Two Cultures: Deep and Statistical Learning A. Bhadra J. Datta Nicholas G. Polson Vadim Sokolov Jianeng Xu BDL 48 9 0 22 Oct 2021
Marginally calibrated response distributions for end-to-end learning in autonomous driving Clara Hoffmann Nadja Klein 11 2 0 03 Oct 2021
Bayesian graph convolutional neural networks via tempered MCMC Rohitash Chandra A. Bhagat Manavendra Maharana P. Krivitsky GNN BDL 28 16 0 17 Apr 2021
Revisiting Bayesian Autoencoders with MCMC Rohitash Chandra Mahir Jain Manavendra Maharana P. Krivitsky UQCV BDL 42 17 0 13 Apr 2021
Bayesian Neural Networks for Virtual Flow Metering: An Empirical Study B. Grimstad M. Hotvedt Anders T. Sandnes O. Kolbjørnsen Lars Imsland 37 21 0 02 Feb 2021
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges Moloud Abdar Farhad Pourpanah Sadiq Hussain Dana Rezazadegan Li Liu ... Xiaochun Cao Abbas Khosravi U. Acharya V. Makarenkov S. Nahavandi BDL UQCV 56 1,883 0 12 Nov 2020
Computer Model Calibration with Time Series Data using Deep Learning and Quantile Regression S. Bhatnagar Won Chang Jiali Wang AI4TS 17 7 0 29 Aug 2020
Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users Laurent Valentin Jospin Wray L. Buntine F. Boussaïd Hamid Laga Bennamoun OOD BDL UQCV 29 610 0 14 Jul 2020
A Bayesian - Deep Learning model for estimating Covid-19 evolution in Spain S. Cabras 32 25 0 20 May 2020
Prior choice affects ability of Bayesian neural networks to identify unknowns D. Silvestro Tobias Andermann UQCV BDL 16 23 0 11 May 2020
Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks M. Dixon AI4TS 31 14 0 09 Apr 2020
Uncertainty Quantification for Sparse Deep Learning Yuexi Wang Veronika Rockova BDL UQCV 36 31 0 26 Feb 2020
Challenges in Markov chain Monte Carlo for Bayesian neural networks Theodore Papamarkou Jacob D. Hinkle M. T. Young D. Womble BDL 44 50 0 15 Oct 2019
Marginally-calibrated deep distributional regression Nadja Klein David J. Nott M. Smith UQCV 37 14 0 26 Aug 2019
Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma S. Berchuck Felipe A. Medeiros S. Mukherjee BDL 34 3 0 24 Aug 2019
Seeing Convolution Through the Eyes of Finite Transformation Semigroup Theory: An Abstract Algebraic Interpretation of Convolutional Neural Networks Andrew Hryniowski A. Wong 16 0 0 26 May 2019
Leveraging Uncertainty in Deep Learning for Selective Classification M. Yildirim M. Ozer H. Davulcu 16 10 0 23 May 2019
Data Augmentation for Bayesian Deep Learning YueXing Wang Nicholas G. Polson Vadim Sokolov UQCV BDL 30 3 0 22 Mar 2019
Comparison of Deep Neural Networks and Deep Hierarchical Models for Spatio-Temporal Data C. Wikle BDL 40 19 0 22 Feb 2019
Understanding Priors in Bayesian Neural Networks at the Unit Level M. Vladimirova Jakob Verbeek Pablo Mesejo Julyan Arbel BDL UQCV 11 4 0 11 Oct 2018
Deep Learning: Computational Aspects Nicholas G. Polson Vadim Sokolov PINN BDL AI4CE 29 14 0 26 Aug 2018
Deep Learning for Energy Markets Michael Polson Vadim Sokolov AI4TS 19 26 0 16 Aug 2018
Deep Learning Nicholas G. Polson Vadim Sokolov AI4CE BDL 37 1 0 20 Jul 2018
Bayesian Deep Net GLM and GLMM Minh-Ngoc Tran Nghia Nguyen David J. Nott Robert Kohn BDL 17 73 0 25 May 2018
Posterior Concentration for Sparse Deep Learning Nicholas G. Polson Veronika Rockova UQCV BDL 30 88 0 24 Mar 2018
Predictive Learning: Using Future Representation Learning Variantial Autoencoder for Human Action Prediction Runsheng Yu Zhenyu Shi Qiongxiong Ma Laiyun Qing 3DH DRL 31 4 0 25 Nov 2017
Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data Patrick L. McDermott C. Wikle BDL UQCV 37 96 0 02 Nov 2017
Deep Learning for Spatio-Temporal Modeling: Dynamic Traffic Flows and High Frequency Trading M. Dixon Nicholas G. Polson Vadim Sokolov AI4TS 35 67 0 27 May 2017
Uncertainty quantification for the horseshoe S. V. D. Pas Botond Szabó A. van der Vaart 38 73 0 07 Jul 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 287 9,156 0 06 Jun 2015