
Time Series Forecasting With Deep Learning: A Survey
Numerous deep learning architectures have been developed to accommodate ...
read it

Learning to Discover Novel Visual Categories via Deep Transfer Clustering
We consider the problem of discovering novel object categories in an ima...
read it

PyTorch: An Imperative Style, HighPerformance Deep Learning Library
Deep learning frameworks have often focused on either usability or speed...
read it

Selflabelling via simultaneous clustering and representation learning
Combining clustering and representation learning is one of the most prom...
read it

Deep Learning in the Era of Edge Computing: Challenges and Opportunities
The era of edge computing has arrived. Although the Internet is the back...
read it

The Hanabi Challenge: A New Frontier for AI Research
From the early days of computing, games have been important testbeds for...
read it

Deep Coordination Graphs
This paper introduces the deep coordination graph (DCG) for collaborativ...
read it

A Survey of Reinforcement Learning Informed by Natural Language
To be successful in realworld tasks, Reinforcement Learning (RL) needs ...
read it

AttentionGAN: Unpaired ImagetoImage Translation using AttentionGuided Generative Adversarial Networks
Stateoftheart methods in the unpaired imagetoimage translation are ...
read it

Kernelbased Graph Learning from Smooth Signals: A Functional Viewpoint
The problem of graph learning concerns the construction of an explicit t...
read it

Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study
Background: The trend towards largescale studies including population i...
read it

Deep Residual Reinforcement Learning
We revisit residual algorithms in both modelfree and modelbased reinfo...
read it

A Survey on Contextual Embeddings
Contextual embeddings, such as ELMo and BERT, move beyond global word re...
read it

Improving SAT Solver Heuristics with Graph Networks and Reinforcement Learning
We present GQSAT, a branching heuristic in a Boolean SAT solver trained ...
read it

SelectFusion: A Generic Framework to Selectively Learn Multisensory Fusion
Autonomous vehicles and mobile robotic systems are typically equipped wi...
read it

Exploratory Combinatorial Optimization with Reinforcement Learning
Many realworld problems can be reduced to combinatorial optimization on...
read it

Count, Crop and Recognise: FineGrained Recognition in the Wild
The goal of this paper is to label all the animal individuals present in...
read it

Rational neural networks
We consider neural networks with rational activation functions. The choi...
read it

A Geometric Approach to Obtain a Bird's Eye View from an Image
The objective of this paper is to rectify any monocular image by computi...
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

Maximizing Information Gain in Partially Observable Environments via Prediction Reward
Information gathering in a partially observable environment can be formu...
read it

Adjusting for Confounding in Unsupervised Latent Representations of Images
Biological imaging data are often partially confounded or contain unwant...
read it

The VGG Image Annotator (VIA)
Manual image annotation, such as defining and labelling regions of inter...
read it

Generalized OffPolicy ActorCritic
We propose a new objective, the counterfactual objective, unifying exist...
read it

Asymmetric Generative Adversarial Networks for ImagetoImage Translation
Stateoftheart models for unpaired imagetoimage translation with Gen...
read it

Meta Learning Deep Visual Words for Fast Video Object Segmentation
Meta learning has attracted a lot of attention recently. In this paper, ...
read it

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

Knowing The What But Not The Where in Bayesian Optimization
Bayesian optimization has demonstrated impressive success in finding the...
read it

Learning Retrospective Knowledge with Reverse Reinforcement Learning
We present a Reverse Reinforcement Learning (Reverse RL) approach for re...
read it

Transflow Learning: Repurposing Flow Models Without Retraining
It is well known that deep generative models have a rich latent space, a...
read it

Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar
This paper presents a selfsupervised framework for learning to detect r...
read it

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

Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search
Reliable yet efficient evaluation of generalisation performance of a pro...
read it

Transfer Learning for Relation Extraction via RelationGated Adversarial Learning
Relation extraction aims to extract relational facts from sentences. Pre...
read it

Rethinking SemiSupervised Learning in VAEs
We present an alternative approach to semisupervision in variational au...
read it

Dropout Distillation for Efficiently Estimating Model Confidence
We propose an efficient way to output better calibrated uncertainty scor...
read it

Loaded DiCE: Trading off Bias and Variance in AnyOrder Score Function Estimators for Reinforcement Learning
Gradientbased methods for optimisation of objectives in stochastic sett...
read it

SelfVIO: SelfSupervised Deep Monocular VisualInertial Odometry and Depth Estimation
In the last decade, numerous supervised deep learning approaches requiri...
read it

A Maximum Entropy approach to Massive Graph Spectra
Graph spectral techniques for measuring graph similarity, or for learnin...
read it

Uncertainty Evaluation Metric for Brain Tumour Segmentation
In this paper, we develop a metric designed to assess and rank uncertain...
read it

Temporal Fusion Transformers for Interpretable Multihorizon Time Series Forecasting
Multihorizon forecasting problems often contain a complex mix of inputs...
read it

Unifying Training and Inference for Panoptic Segmentation
We present an endtoend network to bridge the gap between training and ...
read it

Evolving Neural Networks through a Reverse Encoding Tree
NeuroEvolution is one of the most competitive evolutionary learning fram...
read it

Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives
Deep latent variable models have become a popular model choice due to th...
read it

A Generative 3D Facial Model by Adversarial Training
We consider datadriven generative models for the 3D face, and focus in ...
read it

Surprising Effectiveness of FewImage Unsupervised Feature Learning
Stateoftheart methods for unsupervised representation learning can tr...
read it

Up to two billion times acceleration of scientific simulations with deep neural architecture search
Computer simulations are invaluable tools for scientific discovery. Howe...
read it

Effective Diversity in PopulationBased Reinforcement Learning
Maintaining a population of solutions has been shown to increase explora...
read it

OneShot Bayes Opt with Probabilistic Population Based Training
Selecting optimal hyperparameters is a key challenge in machine learning...
read it

Unpacking Information Bottlenecks: Unifying InformationTheoretic Objectives in Deep Learning
The information bottleneck (IB) principle offers both a mechanism to exp...
read it
University of Oxford
University of Oxford is an educational institution that provides undergraduate, graduate, research, and distance learning programs. It offers courses in humanities; physical and life sciences; medical sciences; mathematics; social sciences; archaeology and anthropology; biological sciences; biochemistry; chemistry; and economics and management.