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
233 publications
Jie Li
116 publications
Yee Whye Teh
102 publications
Weinan Zhang
97 publications
Jun Liu
95 publications
Sebastian Riedel
82 publications
Ang Li
81 publications
Arthur Gretton
68 publications
Isabelle Augenstein
65 publications
Sébastien Ourselin
57 publications
Ge Wang
56 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

read it
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

read it
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

read it
Unsupervised Knowledge-Transfer for Learned Image Reconstruction

Deep learning-based image reconstruction approaches have demonstrated im...
07/06/2021 ∙ by Riccardo Barbano, et al. ∙ 37 ∙ share

read it
Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search

In this paper, we introduce a new reinforcement learning (RL) based neur...
07/17/2020 ∙ by Yuan Tian, et al. ∙ 29 ∙ share

read it
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

read it
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

read it
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

read it
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

read it
The relationship between dynamic programming and active inference: the discrete, finite-horizon case

Active inference is a normative framework for generating behaviour based...
09/17/2020 ∙ by Lancelot Da Costa, et al. ∙ 18 ∙ share

read it
On Margins and Derandomisation in PAC-Bayes

We develop a framework for derandomising PAC-Bayesian generalisation bou...
07/08/2021 ∙ by Felix Biggs, et al. ∙ 17 ∙ share

read it
Neural Empirical Bayes

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

read it
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

read it
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

read it
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. ∙ 15 ∙ share

read it
Information-Theoretic Bounds on the Moments of the Generalization Error of Learning Algorithms

Generalization error bounds are critical to understanding the performanc...
02/03/2021 ∙ by Gholamali Aminian, et al. ∙ 15 ∙ share

read it
Kernel Instrumental Variable Regression

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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
Synthetic and Real Inputs for Tool Segmentation in Robotic Surgery

Semantic tool segmentation in surgical videos is important for surgical ...
07/17/2020 ∙ by Emanuele Colleoni, et al. ∙ 12 ∙ share

read it
A Foliated View of Transfer Learning

Transfer learning considers a learning process where a new task is solve...
08/02/2020 ∙ by Janith Petangoda, et al. ∙ 12 ∙ share

read it
Upper and Lower Bounds on the Performance of Kernel PCA

Principal Component Analysis (PCA) is a popular method for dimension red...
12/18/2020 ∙ by Maxime Haddouche, et al. ∙ 12 ∙ share

read it
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

read it
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

read it
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

read it
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

read it
ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation

Fully convolutional U-shaped neural networks have largely been the domin...
04/14/2021 ∙ by Martin Ferianc, et al. ∙ 11 ∙ share

read it
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

read it
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

read it
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

read it
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

read it
Tighter risk certificates for neural networks

This paper presents empirical studies regarding training probabilistic n...
07/25/2020 ∙ by Maria Perez-Ortiz, et al. ∙ 10 ∙ share

read it
Generative Modelling of BRDF Textures from Flash Images

We learn a latent space for easy capture, semantic editing, consistent i...
02/23/2021 ∙ by Philipp Henzler, et al. ∙ 10 ∙ share

read it
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

read it
Spatio-temporal Graph-RNN for Point Cloud Prediction

In this paper, we propose an end-to-end learning network to predict futu...
02/15/2021 ∙ by Pedro Gomes, et al. ∙ 9 ∙ share

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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. ∙ 8 ∙ share

read it
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. ∙ 8 ∙ share

read it
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

read it