
Time Series Forecasting With Deep Learning: A Survey
Numerous deep learning architectures have been developed to accommodate ...
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Learning to Discover Novel Visual Categories via Deep Transfer Clustering
We consider the problem of discovering novel object categories in an ima...
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PyTorch: An Imperative Style, HighPerformance Deep Learning Library
Deep learning frameworks have often focused on either usability or speed...
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Selflabelling via simultaneous clustering and representation learning
Combining clustering and representation learning is one of the most prom...
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Deep Learning in the Era of Edge Computing: Challenges and Opportunities
The era of edge computing has arrived. Although the Internet is the back...
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The Hanabi Challenge: A New Frontier for AI Research
From the early days of computing, games have been important testbeds for...
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Deep Coordination Graphs
This paper introduces the deep coordination graph (DCG) for collaborativ...
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A Survey of Reinforcement Learning Informed by Natural Language
To be successful in realworld tasks, Reinforcement Learning (RL) needs ...
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AttentionGAN: Unpaired ImagetoImage Translation using AttentionGuided Generative Adversarial Networks
Stateoftheart methods in the unpaired imagetoimage translation are ...
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Kernelbased Graph Learning from Smooth Signals: A Functional Viewpoint
The problem of graph learning concerns the construction of an explicit t...
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Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study
Background: The trend towards largescale studies including population i...
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Deep Residual Reinforcement Learning
We revisit residual algorithms in both modelfree and modelbased reinfo...
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A Survey on Contextual Embeddings
Contextual embeddings, such as ELMo and BERT, move beyond global word re...
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Improving SAT Solver Heuristics with Graph Networks and Reinforcement Learning
We present GQSAT, a branching heuristic in a Boolean SAT solver trained ...
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SelectFusion: A Generic Framework to Selectively Learn Multisensory Fusion
Autonomous vehicles and mobile robotic systems are typically equipped wi...
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Exploratory Combinatorial Optimization with Reinforcement Learning
Many realworld problems can be reduced to combinatorial optimization on...
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Count, Crop and Recognise: FineGrained Recognition in the Wild
The goal of this paper is to label all the animal individuals present in...
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Rational neural networks
We consider neural networks with rational activation functions. The choi...
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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...
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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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Maximizing Information Gain in Partially Observable Environments via Prediction Reward
Information gathering in a partially observable environment can be formu...
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Adjusting for Confounding in Unsupervised Latent Representations of Images
Biological imaging data are often partially confounded or contain unwant...
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The VGG Image Annotator (VIA)
Manual image annotation, such as defining and labelling regions of inter...
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Generalized OffPolicy ActorCritic
We propose a new objective, the counterfactual objective, unifying exist...
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Asymmetric Generative Adversarial Networks for ImagetoImage Translation
Stateoftheart models for unpaired imagetoimage translation with Gen...
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Meta Learning Deep Visual Words for Fast Video Object Segmentation
Meta learning has attracted a lot of attention recently. In this paper, ...
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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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Knowing The What But Not The Where in Bayesian Optimization
Bayesian optimization has demonstrated impressive success in finding the...
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Learning Retrospective Knowledge with Reverse Reinforcement Learning
We present a Reverse Reinforcement Learning (Reverse RL) approach for re...
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Transflow Learning: Repurposing Flow Models Without Retraining
It is well known that deep generative models have a rich latent space, a...
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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...
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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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Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search
Reliable yet efficient evaluation of generalisation performance of a pro...
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Transfer Learning for Relation Extraction via RelationGated Adversarial Learning
Relation extraction aims to extract relational facts from sentences. Pre...
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Rethinking SemiSupervised Learning in VAEs
We present an alternative approach to semisupervision in variational au...
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Dropout Distillation for Efficiently Estimating Model Confidence
We propose an efficient way to output better calibrated uncertainty scor...
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Loaded DiCE: Trading off Bias and Variance in AnyOrder Score Function Estimators for Reinforcement Learning
Gradientbased methods for optimisation of objectives in stochastic sett...
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SelfVIO: SelfSupervised Deep Monocular VisualInertial Odometry and Depth Estimation
In the last decade, numerous supervised deep learning approaches requiri...
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A Maximum Entropy approach to Massive Graph Spectra
Graph spectral techniques for measuring graph similarity, or for learnin...
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Uncertainty Evaluation Metric for Brain Tumour Segmentation
In this paper, we develop a metric designed to assess and rank uncertain...
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Temporal Fusion Transformers for Interpretable Multihorizon Time Series Forecasting
Multihorizon forecasting problems often contain a complex mix of inputs...
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Unifying Training and Inference for Panoptic Segmentation
We present an endtoend network to bridge the gap between training and ...
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Evolving Neural Networks through a Reverse Encoding Tree
NeuroEvolution is one of the most competitive evolutionary learning fram...
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Fewshot segmentation of medical images based on metalearning with implicit gradients
Classical supervised methods commonly used often suffer from the require...
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Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives
Deep latent variable models have become a popular model choice due to th...
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A Generative 3D Facial Model by Adversarial Training
We consider datadriven generative models for the 3D face, and focus in ...
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Surprising Effectiveness of FewImage Unsupervised Feature Learning
Stateoftheart methods for unsupervised representation learning can tr...
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Up to two billion times acceleration of scientific simulations with deep neural architecture search
Computer simulations are invaluable tools for scientific discovery. Howe...
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Effective Diversity in PopulationBased Reinforcement Learning
Maintaining a population of solutions has been shown to increase explora...
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OneShot Bayes Opt with Probabilistic Population Based Training
Selecting optimal hyperparameters is a key challenge in machine learning...
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