
A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks
Evaluation of Bayesian deep learning (BDL) methods is challenging. We of...
read it

VariBAD: A Very Good Method for BayesAdaptive Deep RL via MetaLearning
Trading off exploration and exploitation in an unknown environment is ke...
read it

Machine Learning for Generalizable Prediction of Flood Susceptibility
Flooding is a destructive and dangerous hazard and climate change appear...
read it

Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning
We use Bayesian convolutional neural networks and a novel generative mod...
read it

Probabilistic SuperResolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties
Machine learning techniques have been successfully applied to superreso...
read it

Learning Sparse Networks Using Targeted Dropout
Neural networks are easier to optimise when they have many more weights ...
read it

Evaluating Bayesian Deep Learning Methods for Semantic Segmentation
Deep learning has been revolutionary for computer vision and semantic se...
read it

Uncertainty Quantification with Statistical Guarantees in EndtoEnd Autonomous Driving Control
Deep neural network controllers for autonomous driving have recently ben...
read it

Try Depth Instead of Weight Correlations: Meanfield is a Less Restrictive Assumption for Deeper Networks
We challenge the longstanding assumption that the meanfield approximati...
read it

On the Importance of Strong Baselines in Bayesian Deep Learning
Like all subfields of machine learning, Bayesian Deep Learning is drive...
read it

A Unifying Bayesian View of Continual Learning
Some machine learning applications require continual learning  where da...
read it

Evaluating Uncertainty Quantification in EndtoEnd Autonomous Driving Control
A rise in popularity of Deep Neural Networks (DNNs), attributed to more ...
read it

Radial Bayesian Neural Networks: Robust Variational Inference In Big Models
We propose Radial Bayesian Neural Networks: a variational distribution f...
read it

Generalizing from a few environments in safetycritical reinforcement learning
Before deploying autonomous agents in the real world, we need to be conf...
read it

Differentially Private Continual Learning
Catastrophic forgetting can be a significant problem for institutions th...
read it

Prediction of GNSS Phase Scintillations: A Machine Learning Approach
A Global Navigation Satellite System (GNSS) uses a constellation of sate...
read it

Adversarial recovery of agent rewards from latent spaces of the limit order book
Inverse reinforcement learning has proved its ability to explain statea...
read it

Towards Robust Evaluations of Continual Learning
Continual learning experiments used in current deep learning papers do n...
read it

Towards Inverse Reinforcement Learning for Limit Order Book Dynamics
Multiagent learning is a promising method to simulate aggregate competi...
read it

BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
We develop BatchBALD, a tractable approximation to the mutual informatio...
read it

Real Time Image Saliency for Black Box Classifiers
In this work we develop a fast saliency detection method that can be app...
read it

Concrete Dropout
Dropout is used as a practical tool to obtain uncertainty estimates in l...
read it

Dropout Inference in Bayesian Neural Networks with Alphadivergences
To obtain uncertainty estimates with realworld Bayesian deep learning m...
read it

MultiTask Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Numerous deep learning applications benefit from multitask learning wit...
read it

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
There are two major types of uncertainty one can model. Aleatoric uncert...
read it

Deep Bayesian Active Learning with Image Data
Even though active learning forms an important pillar of machine learnin...
read it

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
Recurrent neural networks (RNNs) stand at the forefront of many recent d...
read it

Dirichlet Fragmentation Processes
Tree structures are ubiquitous in data across many domains, and many dat...
read it

Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
Convolutional neural networks (CNNs) work well on large datasets. But la...
read it

Dropout as a Bayesian Approximation: Appendix
We show that a neural network with arbitrary depth and nonlinearities, ...
read it

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Deep learning tools have gained tremendous attention in applied machine ...
read it

Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs
Standard sparse pseudoinput approximations to the Gaussian process (GP)...
read it

Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data
Multivariate categorical data occur in many applications of machine lear...
read it

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...
read it

Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models
Gaussian processes (GPs) are a powerful tool for probabilistic inference...
read it

Vprop: Variational Inference using RMSprop
Many computationallyefficient methods for Bayesian deep learning rely o...
read it

A Generative Deep Recurrent Model for Exchangeable Data
We present a novel model architecture which leverages deep learning tool...
read it

Understanding Measures of Uncertainty for Adversarial Example Detection
Measuring uncertainty is a promising technique for detecting adversarial...
read it

LossCalibrated Approximate Inference in Bayesian Neural Networks
Current approaches in approximate inference for Bayesian neural networks...
read it

Idealised Bayesian Neural Networks Cannot Have Adversarial Examples: Theoretical and Empirical Study
We prove that idealised discriminative Bayesian neural networks, capturi...
read it

Fast and Scalable Bayesian Deep Learning by WeightPerturbation in Adam
Uncertainty computation in deep learning is essential to design robust a...
read it

An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
Machine learning is now used in many areas of astrophysics, from detecti...
read it

Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder
High energy particles originating from solar activity travel along the t...
read it

SingleFrame SuperResolution of Solar Magnetograms: Investigating PhysicsBased Metrics & Losses
Breakthroughs in our understanding of physical phenomena have traditiona...
read it

AutoCalibration of Remote Sensing Solar Telescopes with Deep Learning
As a part of NASA's Heliophysics System Observatory (HSO) fleet of satel...
read it

Using UNets to Create HighFidelity Virtual Observations of the Solar Corona
Understanding and monitoring the complex and dynamic processes of the Su...
read it
Yarin Gal
is this you? claim profile
Associate Professor of Machine Learning at the Computer Science department at University of Oxford