UCL

∙ London , United Kingdom ∙ 1 follower

University College London is a public research university in London, England, and a constituent college of the federal University of London.

Featured Researchers

Jun Wang
156 publications
Yee Whye Teh
87 publications
Weinan Zhang
79 publications
Sebastian Riedel
61 publications
Ang Li
58 publications
Sébastien Ourselin
57 publications
Arthur Gretton
54 publications
Ge Wang
45 publications
David Silver
43 publications
Massimiliano Pontil
43 publications
Isabelle Augenstein
40 publications

TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning

We present TorchIO, an open-source Python library for efficient loading,...
03/09/2020 ∙ by Fernando Pérez-García, et al. ∙ 121 ∙ share

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Degenerative Adversarial NeuroImage Nets for 3D Simulations: Application in Longitudinal MRI

The recent success of deep learning together with the availability of la...
12/03/2019 ∙ by Daniele Ravì, et al. ∙ 43 ∙ share

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A Survey on Contextual Embeddings

Contextual embeddings, such as ELMo and BERT, move beyond global word re...
03/16/2020 ∙ by Qi Liu, et al. ∙ 38 ∙ share

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Lautum Regularization for Semi-supervised Transfer Learning

Transfer learning is a very important tool in deep learning as it allows...
04/02/2019 ∙ by Daniel Jakubovitz, et al. ∙ 24 ∙ share

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Margin Maximization as Lossless Maximal Compression

The ultimate goal of a supervised learning algorithm is to produce model...
01/28/2020 ∙ by Nikolaos Nikolaou, et al. ∙ 23 ∙ share

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Disentangling Interpretable Generative Parameters of Random and Real-World Graphs

While a wide range of interpretable generative procedures for graphs exi...
10/12/2019 ∙ by Niklas Stoehr, et al. ∙ 22 ∙ share

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PiNet: A Permutation Invariant Graph Neural Network for Graph Classification

We propose an end-to-end deep learning learning model for graph classifi...
05/08/2019 ∙ by Peter Meltzer, et al. ∙ 20 ∙ share

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Neural Empirical Bayes

We formulate a novel framework that unifies kernel density estimation an...
03/06/2019 ∙ by Saeed Saremi, et al. ∙ 16 ∙ share

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Improving the Robustness of Graphs through Reinforcement Learning and Graph Neural Networks

Graphs can be used to represent and reason about real world systems. A v...
01/30/2020 ∙ by Victor-Alexandru Darvariu, et al. ∙ 15 ∙ share

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PAC-Bayes Analysis Beyond the Usual Bounds

We focus on a stochastic learning model where the learner observes a fin...
06/23/2020 ∙ by Omar Rivasplata, et al. ∙ 15 ∙ share

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Kernel Instrumental Variable Regression

Instrumental variable regression is a strategy for learning causal relat...
06/01/2019 ∙ by Rahul Singh, et al. ∙ 14 ∙ share

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SentiMATE: Learning to play Chess through Natural Language Processing

We present SentiMATE, a novel end-to-end Deep Learning model for Chess, ...
07/18/2019 ∙ by Isaac Kamlish, et al. ∙ 14 ∙ share

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Predicting Engagement in Video Lectures

The explosion of Open Educational Resources (OERs) in the recent years c...
05/31/2020 ∙ by Sahan Bulathwela, et al. ∙ 14 ∙ share

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Deep active inference agents using Monte-Carlo methods

Active inference is a Bayesian framework for understanding biological in...
06/07/2020 ∙ by Zafeirios Fountas, et al. ∙ 14 ∙ share

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Learning a Neural 3D Texture Space from 2D Exemplars

We propose a generative model of 2D and 3D natural textures with diversi...
12/09/2019 ∙ by Philipp Henzler, et al. ∙ 13 ∙ share

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Multi-Task Recurrent Neural Network for Surgical Gesture Recognition and Progress Prediction

Surgical gesture recognition is important for surgical data science and ...
03/10/2020 ∙ by Beatrice van Amsterdam, et al. ∙ 13 ∙ share

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An Algorithm for Learning Shape and Appearance Models without Annotations

This paper presents a framework for automatically learning shape and app...
07/27/2018 ∙ by John Ashburner, et al. ∙ 12 ∙ share

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Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning

Humans are capable of attributing latent mental contents such as beliefs...
01/26/2019 ∙ by Ying Wen, et al. ∙ 12 ∙ share

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Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels

The performance of multi-task learning in Convolutional Neural Networks ...
08/26/2019 ∙ by Felix J. S. Bragman, et al. ∙ 12 ∙ share

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Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning

Social psychology and real experiences show that cognitive consistency p...
12/03/2019 ∙ by Hangyu Mao, et al. ∙ 11 ∙ share

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Better Boosting with Bandits for Online Learning

Probability estimates generated by boosting ensembles are poorly calibra...
01/16/2020 ∙ by Nikolaos Nikolaou, et al. ∙ 11 ∙ share

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Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE

The increasing efficiency and compactness of deep learning architectures...
02/13/2020 ∙ by Petru-Daniel Tudosiu, et al. ∙ 11 ∙ share

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Differentiable PAC-Bayes Objectives with Partially Aggregated Neural Networks

We make three related contributions motivated by the challenge of traini...
06/22/2020 ∙ by Felix Biggs, et al. ∙ 11 ∙ share

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On the Importance of Strong Baselines in Bayesian Deep Learning

Like all sub-fields of machine learning, Bayesian Deep Learning is drive...
11/23/2018 ∙ by Jishnu Mukhoti, et al. ∙ 10 ∙ share

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Optimizing Object-based Perception and Control by Free-Energy Principle

One of the well-known formulations of human perception is a hierarchical...
03/04/2019 ∙ by Minne Li, et al. ∙ 10 ∙ share

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Methods and open-source toolkit for analyzing and visualizing challenge results

Biomedical challenges have become the de facto standard for benchmarking...
10/11/2019 ∙ by Manuel Wiesenfarth, et al. ∙ 10 ∙ share

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TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources

The recent advances in computer-assisted learning systems and the availa...
11/21/2019 ∙ by Sahan Bulathwela, et al. ∙ 10 ∙ share

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Optimizing CNN-based Hyperspectral ImageClassification on FPGAs

Hyperspectral image (HSI) classification has been widely adopted in appl...
06/27/2019 ∙ by Shuanglong Liu, et al. ∙ 9 ∙ share

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Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds

We propose an efficient and effective method to learn convolutions for n...
06/05/2018 ∙ by Pedro Hermosilla, et al. ∙ 8 ∙ share

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Learning Shared Dynamics with Meta-World Models

Humans have consciousness as the ability to perceive events and objects:...
11/05/2018 ∙ by Lisheng Wu, et al. ∙ 8 ∙ share

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Modelling urban networks using Variational Autoencoders

A long-standing question for urban and regional planners pertains to the...
05/14/2019 ∙ by Kira Kempinska, et al. ∙ 8 ∙ share

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Dynamic Face Video Segmentation via Reinforcement Learning

For real-time semantic video segmentation, most recent works utilise a d...
07/02/2019 ∙ by Yujiang Wang, et al. ∙ 8 ∙ share

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SynthCity: A large scale synthetic point cloud

With deep learning becoming a more prominent approach for automatic clas...
07/10/2019 ∙ by David Griffiths, et al. ∙ 8 ∙ share

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Empirical Bayesian Mixture Models for Medical Image Translation

Automatically generating one medical imaging modality from another is kn...
08/16/2019 ∙ by Mikael Brudfors, et al. ∙ 8 ∙ share

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A Learning Strategy for Contrast-agnostic MRI Segmentation

We present a deep learning strategy that enables, for the first time, co...
03/04/2020 ∙ by Benjamin Billot, et al. ∙ 8 ∙ share

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Multiple-Identity Image Attacks Against Face-based Identity Verification

Facial verification systems are vulnerable to poisoning attacks that mak...
06/20/2019 ∙ by Jerone T. A. Andrews, et al. ∙ 7 ∙ share

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Reproducibility of an airway tapering measurement in CT with application to bronchiectasis

Purpose: This paper proposes a pipeline to acquire a scalar tapering mea...
09/16/2019 ∙ by Kin Quan, et al. ∙ 7 ∙ share

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Towards an Integrative Educational Recommender for Lifelong Learners

One of the most ambitious use cases of computer-assisted learning is to ...
12/03/2019 ∙ by Sahan Bulathwela, et al. ∙ 7 ∙ share

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There is Strength in Numbers: Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training

Natural Language Inference (NLI) datasets contain annotation artefacts r...
04/16/2020 ∙ by Joe Stacey, et al. ∙ 7 ∙ share

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An Auto-Encoder Strategy for Adaptive Image Segmentation

Deep neural networks are powerful tools for biomedical image segmentatio...
04/29/2020 ∙ by Evan M. Yu, et al. ∙ 7 ∙ share

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VTrails: Inferring Vessels with Geodesic Connectivity Trees

The analysis of vessel morphology and connectivity has an impact on a nu...
06/08/2018 ∙ by Stefano Moriconi, et al. ∙ 6 ∙ share

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Automated Performance Assessment in Transoesophageal Echocardiography with Convolutional Neural Networks

Transoesophageal echocardiography (TEE) is a valuable diagnostic and mon...
06/13/2018 ∙ by Evangelos B. Mazomenos, et al. ∙ 6 ∙ share

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Higher Order of Motion Magnification for Vessel Localisation in Surgical Video

Locating vessels during surgery is critical for avoiding inadvertent dam...
06/13/2018 ∙ by Mirek Janatka, et al. ∙ 6 ∙ share

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Linkage between Piecewise Constant Mumford-Shah model and ROF model and its virtue in image segmentation

The piecewise constant Mumford-Shah (PCMS) model and the Rudin-Osher-Fat...
07/26/2018 ∙ by Xiaohao Cai, et al. ∙ 6 ∙ share

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3D multirater RCNN for multimodal multiclass detection and characterisation of extremely small objects

Extremely small objects (ESO) have become observable on clinical routine...
12/21/2018 ∙ by Carole H Sudre, et al. ∙ 6 ∙ share

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Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations

Graph classification is a significant problem in many scientific domains...
02/22/2019 ∙ by Marcelo Daniel Gutierrez Mallea, et al. ∙ 6 ∙ share

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A review on deep learning techniques for 3D sensed data classification

Over the past decade deep learning has driven progress in 2D image under...
07/09/2019 ∙ by David Griffiths, et al. ∙ 6 ∙ share

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Deep Sequential Mosaicking of Fetoscopic Videos

Twin-to-twin transfusion syndrome treatment requires fetoscopic laser ph...
07/15/2019 ∙ by Sophia Bano, et al. ∙ 6 ∙ share

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Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement

Deep learning (DL) has shown great potential in medical image enhancemen...
07/31/2019 ∙ by Ryutaro Tanno, et al. ∙ 6 ∙ share

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NeuroMask: Explaining Predictions of Deep Neural Networks through Mask Learning

Deep Neural Networks (DNNs) deliver state-of-the-art performance in many...
08/05/2019 ∙ by Moustafa Alzantot, et al. ∙ 6 ∙ share

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