This study reviews recent advances in UQ methods used in deep learning and investigates the application of these methods in reinforcement learning (RL), and outlines a few important applications of UZ methods.
Authors
Farhad Pourpanah
2 papers
Moloud Abdar
1 papers
Sadiq Hussain
1 papers
Dana Rezazadegan
1 papers
Li Liu
1 papers
M. Ghavamzadeh
1 papers
P. Fieguth
5 papers
Xiaochun Cao
3 papers
A. Khosravi
2 papers
U. R. Acharya
1 papers
V. Makarenkov
1 papers
S. Nahavandi
3 papers
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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks
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U-CAM: Visual Explanation Using Uncertainty Based Class Activation Maps
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Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes
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Predictable Uncertainty-Aware Unsupervised Deep Anomaly Segmentation
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Quality of Uncertainty Quantification for Bayesian Neural Network Inference
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Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applications
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221
Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles
222
Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment
223
Epistemic Risk-Sensitive Reinforcement Learning
224
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes
225
Efficient Priors for Scalable Variational Inference in Bayesian Deep Neural Networks
226
Efficient Evaluation-Time Uncertainty Estimation by Improved Distillation
227
Quantifying Intrinsic Uncertainty in Classification via Deep Dirichlet Mixture Networks
228
Analyzing the role of model uncertainty for electronic health records
229
DropConnect is effective in modeling uncertainty of Bayesian deep networks
230
PHiSeg: Capturing Uncertainty in Medical Image Segmentation
231
MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation
232
Uncertainty-guided Continual Learning with Bayesian Neural Networks
233
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
234
Uncertainty-based Graph Convolutional Networks for Organ Segmentation Refinement
235
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
236
Assessing the Robustness of Bayesian Dark Knowledge to Posterior Uncertainty
237
Bayesian Prior Networks with PAC Training
238
ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning
239
A Bayesian Neural Net to Segment Images with Uncertainty Estimates and Good Calibration
240
Versatile Multiple Choice Learning and Its Application to Vision Computing
241
Uncertainty Guided Multi-Scale Residual Learning-Using a Cycle Spinning CNN for Single Image De-Raining
242
Segmentation Certainty Through Uncertainty: Uncertainty-Refined Binary Volumetric Segmentation Under Multifactor Domain Shift
243
Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction
244
Deep Modular Co-Attention Networks for Visual Question Answering
245
Learning Sparse Networks Using Targeted Dropout
246
ActiveHARNet: Towards On-Device Deep Bayesian Active Learning for Human Activity Recognition
247
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness
248
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks
249
Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections
250
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities
251
Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT
252
Uncertainty-based Continual Learning with Adaptive Regularization
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Capsule Routing via Variational Bayes
254
ODE$^2$VAE: Deep generative second order ODEs with Bayesian neural networks
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On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks
256
Deep spectral learning for label-free optical imaging oximetry with uncertainty quantification
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Prediction of Disease Progression in Multiple Sclerosis Patients using Deep Learning Analysis of MRI Data
258
Estimating Risk and Uncertainty in Deep Reinforcement Learning
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Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
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Ensemble Model Patching: A Parameter-Efficient Variational Bayesian Neural Network
261
Reliable deep-learning-based phase imaging with uncertainty quantification: erratum
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Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning
263
Modeling and Planning Under Uncertainty Using Deep Neural Networks
264
Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions
265
Output-Constrained Bayesian Neural Networks
266
CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features
267
Uncertainty Modeling of Contextual-Connections Between Tracklets for Unconstrained Video-Based Face Recognition
268
Interpretable Deep Gaussian Processes with Moments
269
Exploiting Uncertainty of Deep Neural Networks for Improving Segmentation Accuracy in MRI Images
270
Ensemble Distribution Distillation
271
"Why Should You Trust My Explanation?" Understanding Uncertainty in LIME Explanations
272
Bayesian Generative Active Deep Learning
273
Probabilistic Face Embeddings
274
Uncertainty-Driven Semantic Segmentation through Human-Machine Collaborative Learning
275
A Bayesian Perspective on the Deep Image Prior
276
Exploring Uncertainty Measures for Image-caption Embedding-and-retrieval Task
277
Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving
278
Minimum Uncertainty Based Detection of Adversaries in Deep Neural Networks
279
A Variational Auto-Encoder Model for Stochastic Point Processes
280
Decision Making under Deep Uncertainty: From Theory to Practice
281
Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming
282
Correlated Parameters to Accurately Measure Uncertainty in Deep Neural Networks
283
Variational Adversarial Active Learning
284
From Variational to Deterministic Autoencoders
285
Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models
286
Combining Model and Parameter Uncertainty in Bayesian Neural Networks
287
Crowd Counting with Decomposed Uncertainty
288
Functional Variational Bayesian Neural Networks
289
Temporal Logics Over Finite Traces with Uncertainty
290
BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors
291
Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective
292
Variational Inference to Measure Model Uncertainty in Deep Neural Networks
293
Bayesian Image Classification with Deep Convolutional Gaussian Processes
294
Translation Insensitivity for Deep Convolutional Gaussian Processes
295
Active Perception in Adversarial Scenarios using Maximum Entropy Deep Reinforcement Learning
296
The MBPEP: a deep ensemble pruning algorithm providing high quality uncertainty prediction
297
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
298
A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion From Heartbeat
299
Reducing Uncertainty in Undersampled MRI Reconstruction With Active Acquisition
300
Unsupervised Data Uncertainty Learning in Visual Retrieval Systems
301
A Simple Baseline for Bayesian Uncertainty in Deep Learning
302
Predictive Uncertainty Quantification with Compound Density Networks
303
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning
304
Uncertainty Quantification in Deep MRI Reconstruction
305
Functional Regularisation for Continual Learning using Gaussian Processes
306
Using Pre-Training Can Improve Model Robustness and Uncertainty
307
Variational Smoothing in Recurrent Neural Network Language Models
308
U2-Net: A Bayesian U-Net Model With Epistemic Uncertainty Feedback For Photoreceptor Layer Segmentation In Pathological OCT Scans
309
Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification
310
Striking the Right Balance With Uncertainty
311
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data
312
Decoupled Certainty-Driven Consistency Loss for Semi-supervised Learning
313
Learning and Reasoning for Robot Sequential Decision Making under Uncertainty
314
Posterior inference unchained with EL_2O
315
Neural RGB®D Sensing: Depth and Uncertainty From a Video Camera
316
Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic
317
Reliable deep-learning-based phase imaging with uncertainty quantification.
318
The need for uncertainty quantification in machine-assisted medical decision making
319
Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization
320
Distribution-free uncertainty quantification for kernel methods by gradient perturbations
321
Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting
322
What's to Know? Uncertainty as a Guide to Asking Goal-Oriented Questions
323
Accuracy, uncertainty, and adaptability of automatic myocardial ASL segmentation using deep CNN
324
Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
325
Evaluating Bayesian Deep Learning Methods for Semantic Segmentation
326
3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training
327
The Relevance of Bayesian Layer Positioning to Model Uncertainty in Deep Bayesian Active Learning
328
Uncertainty-Aware Audiovisual Activity Recognition Using Deep Bayesian Variational Inference
329
BDLOB: Bayesian Deep Convolutional Neural Networks for Limit Order Books
330
Bayesian QuickNAT: Model uncertainty in deep whole-brain segmentation for structure-wise quality control
331
On the Importance of Strong Baselines in Bayesian Deep Learning
332
Quantifying Uncertainties in Natural Language Processing Tasks
333
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient
334
Adversarial Uncertainty Quantification in Physics-Informed Neural Networks
335
Wasserstein Variational Gradient Descent: From Semi-Discrete Optimal Transport to Ensemble Variational Inference
336
Single-Model Uncertainties for Deep Learning
337
Frequentist uncertainty estimates for deep learning
338
Uncertainty Estimation for Deep Neural Object Detectors in Safety-Critical Applications
339
Stochastic Normalizations as Bayesian Learning
340
Towards Principled Uncertainty Estimation for Deep Neural Networks
341
A Gaussian Process perspective on Convolutional Neural Networks
342
Safe Reinforcement Learning With Model Uncertainty Estimates
343
The Deep Weight Prior
344
Successor Uncertainties: exploration and uncertainty in temporal difference learning
345
Uncertainty in Neural Networks: Approximately Bayesian Ensembling
346
Variational Bayesian Monte Carlo
347
Predictive Uncertainty through Quantization
348
Uncertainty in Neural Networks: Bayesian Ensembling
349
Deep convolutional Gaussian processes
350
Deep Anomaly Detection with Outlier Exposure
351
Bayesian Policy Optimization for Model Uncertainty
352
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network
353
Deterministic Variational Inference for Robust Bayesian Neural Networks
354
Amortized Bayesian Meta-Learning
355
Inhibited Softmax for Uncertainty Estimation in Neural Networks
356
Deep Ensemble Bayesian Active Learning : Addressing the Mode Collapse issue in Monte Carlo dropout via Ensembles
357
Modeling Uncertainty with Hedged Instance Embedding
358
Bounding Box Regression With Uncertainty for Accurate Object Detection
359
Online Deep Ensemble Learning for Predicting Citywide Human Mobility
360
Deep Network Uncertainty Maps for Indoor Navigation
361
Bayesian Structure Learning by Recursive Bootstrap
362
MotherNets: Rapid Deep Ensemble Learning
363
Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study
364
Prediction and Uncertainty Quantification of Daily Airport Flight Delays
365
Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation
366
Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors
367
Efficient Uncertainty Estimation for Semantic Segmentation in Videos
368
Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks
369
Uncertainty and Interpretability in Convolutional Neural Networks for Semantic Segmentation of Colorectal Polyps
370
Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks
371
Direct Uncertainty Prediction for Medical Second Opinions
372
Conditional Neural Processes
373
Leveraging Uncertainty Estimates for Predicting Segmentation Quality
374
Noise Contrastive Priors for Functional Uncertainty
375
Accurate Uncertainties for Deep Learning Using Calibrated Regression
376
Deep echo state networks with uncertainty quantification for spatio‐temporal forecasting
377
Bayesian Prediction of Future Street Scenes through Importance Sampling based Optimization
378
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo
379
A Probabilistic U-Net for Segmentation of Ambiguous Images
380
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
381
Bayesian Model-Agnostic Meta-Learning
382
On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation
383
Evidential Deep Learning to Quantify Classification Uncertainty
384
The Power of Ensembles for Active Learning in Image Classification
385
Multi-level Fusion Based 3D Object Detection from Monocular Images
386
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
387
Uncertainty Gated Network for Land Cover Segmentation
388
Lightweight Probabilistic Deep Networks
389
Deep Learning Under Privileged Information Using Heteroscedastic Dropout
390
Calibrating Deep Convolutional Gaussian Processes
391
Large-Scale Distance Metric Learning with Uncertainty
392
Meta-Learning Probabilistic Inference for Prediction
393
Uncertainty-Aware Attention for Reliable Interpretation and Prediction
394
Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers
395
Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation
396
A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation
397
UNCERTAINTY MODELING AND INTERPRETABILITY IN CONVOLUTIONAL NEURAL NETWORKS FOR POLYP SEGMENTATION
398
Spatial Uncertainty Sampling for End-to-End Control
399
Improving predictive uncertainty estimation using Dropout–Hamiltonian Monte Carlo
400
Deep Directional Statistics: Pose Estimation with Uncertainty Quantification
401
To Trust Or Not To Trust A Classifier
402
Infrared and Visible Image Fusion using a Deep Learning Framework
403
Benchmarking Uncertainty Estimates with Deep Reinforcement Learning for Dialogue Policy Optimisation
404
Adversarial Alignment of Class Prediction Uncertainties for Domain Adaptation
405
A Variational U-Net for Conditional Appearance and Shape Generation
406
Simple Domain Adaptation with Class Prediction Uncertainty Alignment
407
Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation
408
Quality control in radiotherapy-treatment planning using multi-task learning and uncertainty estimation
409
Test-time Data Augmentation for Estimation of Heteroscedastic Aleatoric Uncertainty in Deep Neural Networks
410
YOLOv3: An Incremental Improvement
411
A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization
412
Averaging Weights Leads to Wider Optima and Better Generalization
413
Variance Networks: When Expectation Does Not Meet Your Expectations
414
Deep Bayesian Active Semi-Supervised Learning
415
Hashing with Mutual Information
416
Scalable Bayesian Uncertainty Quantification in Imaging Inverse Problems via Convex Optimization
417
Understanding Measures of Uncertainty for Adversarial Example Detection
418
Analyzing Uncertainty in Neural Machine Translation
419
Predictive Uncertainty Estimation via Prior Networks
420
High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach
421
Structured Uncertainty Prediction Networks
422
Uncertainty Estimates for Optical Flow with Multi-Hypotheses Networks
423
Uncertainty Estimates and Multi-hypotheses Networks for Optical Flow
424
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
425
Bayesian Uncertainty Estimation for Batch Normalized Deep Networks
426
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
427
A Scalable Laplace Approximation for Neural Networks
428
Uncertainty Estimation via Stochastic Batch Normalization
429
Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning
430
Learning Structural Weight Uncertainty for Sequential Decision-Making
431
Bayesian Policy Gradients via Alpha Divergence Dropout Inference
432
Propagating Uncertainty in Multi-Stage Bayesian Convolutional Neural Networks with Application to Pulmonary Nodule Detection
433
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning
434
How Deep Are Deep Gaussian Processes?
435
Long-Term On-board Prediction of People in Traffic Scenes Under Uncertainty
436
Deep-ESN: A Multiple Projection-encoding Hierarchical Reservoir Computing Framework
437
Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory
438
Implicit Weight Uncertainty in Neural Networks
439
Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data
440
Deep Neural Networks as Gaussian Processes
441
Rough extreme learning machine: a new classification method based on uncertainty measure
442
Variational Continual Learning
443
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
444
Uncertainty-driven Imagination for Continuous Deep Reinforcement Learning
445
Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning
446
Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks
447
The Uncertainty Bellman Equation and Exploration
448
Towards Uncertainty-Assisted Brain Tumor Segmentation and Survival Prediction
449
Convolutional Gaussian Processes
450
Uncertainty-Aware Learning from Demonstration Using Mixture Density Networks with Sampling-Free Variance Modeling
451
Uncertainty Quantification, Image Synthesis and Deformation Prediction for Image Registration
452
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
453
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks
454
Bayesian Semisupervised Learning with Deep Generative Models
455
Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy
456
Confident Multiple Choice Learning
457
Attention is All you Need
458
IDK Cascades: Fast Deep Learning by Learning not to Overthink
459
Crowdsourcing Thousands of Specialized Labels: A Bayesian Active Training Approach
460
Deep Learning: A Bayesian Perspective
461
Model Selection in Bayesian Neural Networks via Horseshoe Priors
462
Concrete Dropout
463
Ensemble Sampling
464
Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
465
Addressing uncertainty in atomistic machine learning.
466
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution
467
Doubly Stochastic Variational Inference for Deep Gaussian Processes
468
Bayesian Recurrent Neural Networks
469
Learning Structured Weight Uncertainty in Bayesian Neural Networks
470
MIHash: Online Hashing with Mutual Information
471
Measures of uncertainty for neighborhood rough sets
472
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
473
Deep Bayesian Active Learning with Image Data
474
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
475
A Q-learning-based multi-agent system for data classification
476
A survey on deep learning in medical image analysis
477
Uncertainty-Aware Reinforcement Learning for Collision Avoidance
478
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
479
Bayesian Optimization with Robust Bayesian Neural Networks
480
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
481
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
482
Leveraging uncertainty information from deep neural networks for disease detection
483
Black-Box Alpha Divergence Minimization
484
Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks
485
Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification
486
Hierarchical Question-Image Co-Attention for Visual Question Answering
487
Coresets for Scalable Bayesian Logistic Regression
488
VIME: Variational Information Maximizing Exploration
489
Towards Bayesian Deep Learning: A Survey
490
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors
491
Automatic Differentiation Variational Inference
492
Assumed Density Filtering Methods for Learning Bayesian Neural Networks
493
Bayesian Recurrent Neural Network for Language Modeling
494
Variational Inference: A Review for Statisticians
495
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators
496
A study of active learning methods for named entity recognition in clinical text
497
Bayesian Reinforcement Learning: A Survey
498
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding
499
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
500
Modelling uncertainty in deep learning for camera relocalization
501
Uncertainty Detection in Natural Language Texts
502
Bayesian dark knowledge
503
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
504
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
505
Weight Uncertainty in Neural Networks
506
U-Net: Convolutional Networks for Biomedical Image Segmentation
507
VQA: Visual Question Answering
508
Linear Maximum Margin Classifier for Learning from Uncertain Data
509
Bayesian Sampling Using Stochastic Gradient Thermostats
510
Sentiment analysis: Bayesian Ensemble Learning
511
The Loss Surfaces of Multilayer Networks
512
Efficient object localization using Convolutional Networks
513
Fully convolutional networks for semantic segmentation
514
Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics
515
Avoiding pathologies in very deep networks
516
Stochastic Gradient Hamiltonian Monte Carlo
517
Auto-Encoding Variational Bayes
518
Intriguing properties of neural networks
519
Deep Gaussian Processes
520
An Outline of a Theory of Three-Way Decisions
521
Bayesian Active Learning for Classification and Preference Learning
522
Bayesian Learning via Stochastic Gradient Langevin Dynamics
523
An ensemble uncertainty aware measure for directed hill climbing ensemble pruning
524
Aleatory or epistemic? Does it matter?
525
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
526
Gaussian Processes For Machine Learning
527
Ideal-observer computation in medical imaging with use of Markov-chain Monte Carlo techniques.
528
Matrix Variate Distributions
529
An Introduction to Variational Methods for Graphical Models
530
On Information and Sufficiency
531
Hybrid Monte Carlo
532
Learning internal representations by error propagation