
A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks
Evaluation of Bayesian deep learning (BDL) methods is challenging. We of...
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VariBAD: A Very Good Method for BayesAdaptive Deep RL via MetaLearning
Trading off exploration and exploitation in an unknown environment is ke...
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Machine Learning for Generalizable Prediction of Flood Susceptibility
Flooding is a destructive and dangerous hazard and climate change appear...
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Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning
We use Bayesian convolutional neural networks and a novel generative mod...
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Probabilistic SuperResolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties
Machine learning techniques have been successfully applied to superreso...
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Learning Sparse Networks Using Targeted Dropout
Neural networks are easier to optimise when they have many more weights ...
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Evaluating Bayesian Deep Learning Methods for Semantic Segmentation
Deep learning has been revolutionary for computer vision and semantic se...
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Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network
We propose a method for training a deterministic deep model that can fin...
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Uncertainty Quantification with Statistical Guarantees in EndtoEnd Autonomous Driving Control
Deep neural network controllers for autonomous driving have recently ben...
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Unpacking Information Bottlenecks: Unifying InformationTheoretic Objectives in Deep Learning
The information bottleneck (IB) principle offers both a mechanism to exp...
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Capsule Networks – A Probabilistic Perspective
'Capsule' models try to explicitly represent the poses of objects, enfor...
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Try Depth Instead of Weight Correlations: Meanfield is a Less Restrictive Assumption for Deeper Networks
We challenge the longstanding assumption that the meanfield approximati...
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On the Importance of Strong Baselines in Bayesian Deep Learning
Like all subfields of machine learning, Bayesian Deep Learning is drive...
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A Unifying Bayesian View of Continual Learning
Some machine learning applications require continual learning  where da...
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Evaluating Uncertainty Quantification in EndtoEnd Autonomous Driving Control
A rise in popularity of Deep Neural Networks (DNNs), attributed to more ...
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Radial Bayesian Neural Networks: Robust Variational Inference In Big Models
We propose Radial Bayesian Neural Networks: a variational distribution f...
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Generalizing from a few environments in safetycritical reinforcement learning
Before deploying autonomous agents in the real world, we need to be conf...
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Differentially Private Continual Learning
Catastrophic forgetting can be a significant problem for institutions th...
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Prediction of GNSS Phase Scintillations: A Machine Learning Approach
A Global Navigation Satellite System (GNSS) uses a constellation of sate...
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Adversarial recovery of agent rewards from latent spaces of the limit order book
Inverse reinforcement learning has proved its ability to explain statea...
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On the Benefits of Invariance in Neural Networks
Many real world data analysis problems exhibit invariant structure, and ...
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Towards Robust Evaluations of Continual Learning
Continual learning experiments used in current deep learning papers do n...
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Invariant Causal Prediction for Block MDPs
Generalization across environments is critical to the successful applica...
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Towards Inverse Reinforcement Learning for Limit Order Book Dynamics
Multiagent learning is a promising method to simulate aggregate competi...
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BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
We develop BatchBALD, a tractable approximation to the mutual informatio...
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Real Time Image Saliency for Black Box Classifiers
In this work we develop a fast saliency detection method that can be app...
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Concrete Dropout
Dropout is used as a practical tool to obtain uncertainty estimates in l...
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Dropout Inference in Bayesian Neural Networks with Alphadivergences
To obtain uncertainty estimates with realworld Bayesian deep learning m...
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MultiTask Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Numerous deep learning applications benefit from multitask learning wit...
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What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
There are two major types of uncertainty one can model. Aleatoric uncert...
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Deep Bayesian Active Learning with Image Data
Even though active learning forms an important pillar of machine learnin...
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A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
Recurrent neural networks (RNNs) stand at the forefront of many recent d...
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Dirichlet Fragmentation Processes
Tree structures are ubiquitous in data across many domains, and many dat...
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Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
Convolutional neural networks (CNNs) work well on large datasets. But la...
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Dropout as a Bayesian Approximation: Appendix
We show that a neural network with arbitrary depth and nonlinearities, ...
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Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Deep learning tools have gained tremendous attention in applied machine ...
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Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs
Standard sparse pseudoinput approximations to the Gaussian process (GP)...
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Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data
Multivariate categorical data occur in many applications of machine lear...
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Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models  a Gentle Tutorial
In this tutorial we explain the inference procedures developed for the s...
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Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models
Gaussian processes (GPs) are a powerful tool for probabilistic inference...
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Vprop: Variational Inference using RMSprop
Many computationallyefficient methods for Bayesian deep learning rely o...
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A Generative Deep Recurrent Model for Exchangeable Data
We present a novel model architecture which leverages deep learning tool...
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Understanding Measures of Uncertainty for Adversarial Example Detection
Measuring uncertainty is a promising technique for detecting adversarial...
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LossCalibrated Approximate Inference in Bayesian Neural Networks
Current approaches in approximate inference for Bayesian neural networks...
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Idealised Bayesian Neural Networks Cannot Have Adversarial Examples: Theoretical and Empirical Study
We prove that idealised discriminative Bayesian neural networks, capturi...
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Fast and Scalable Bayesian Deep Learning by WeightPerturbation in Adam
Uncertainty computation in deep learning is essential to design robust a...
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An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
Machine learning is now used in many areas of astrophysics, from detecti...
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Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder
High energy particles originating from solar activity travel along the t...
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SingleFrame SuperResolution of Solar Magnetograms: Investigating PhysicsBased Metrics & Losses
Breakthroughs in our understanding of physical phenomena have traditiona...
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AutoCalibration of Remote Sensing Solar Telescopes with Deep Learning
As a part of NASA's Heliophysics System Observatory (HSO) fleet of satel...
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Yarin Gal
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Associate Professor of Machine Learning at the Computer Science department at University of Oxford