The University of Sheffield

research

∙ 04/08/2021

DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays

The global pandemic of COVID-19 is continuing to have a significant effe...

Michail Mamalakis, et al. ∙

research

∙ 08/03/2021

ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference

Neural language representation models such as BERT, pre-trained on large...

Amit Gajbhiye, et al. ∙

research

∙ 12/21/2020

Unifying Homophily and Heterophily Network Transformation via Motifs

Higher-order proximity (HOP) is fundamental for most network embedding m...

Yan Ge, et al. ∙

research

∙ 09/08/2021

Active Learning by Acquiring Contrastive Examples

Common acquisition functions for active learning use either uncertainty ...

Katerina Margatina, et al. ∙

research

∙ 08/29/2018

Physically-inspired Gaussian processes for transcriptional regulation in Drosophila melanogaster

The regulatory process in Drosophila melanogaster is thoroughly studied ...

Andrés F. López-Lopera, et al. ∙

research

∙ 09/19/2019

Differentially Private Regression and Classification with Sparse Gaussian Processes

A continuing challenge for machine learning is providing methods to perf...

Michael Thomas Smith, et al. ∙

research

∙ 08/02/2020

A Foliated View of Transfer Learning

Transfer learning considers a learning process where a new task is solve...

Janith Petangoda, et al. ∙

research

∙ 10/13/2021

Metaparametric Neural Networks for Survival Analysis

Survival analysis is a critical tool for the modelling of time-to-event ...

Fabio Luis de Mello, et al. ∙

research

∙ 10/10/2018

Non-linear process convolutions for multi-output Gaussian processes

The paper introduces a non-linear version of the process convolution for...

Mauricio A. Álvarez, et al. ∙

research

∙ 10/26/2021

Modular Gaussian Processes for Transfer Learning

We present a framework for transfer learning based on modular variationa...

Pablo Moreno-Muñoz, et al. ∙

research

∙ 03/04/2022

Uncertainty Estimation for Heatmap-based Landmark Localization

Automatic anatomical landmark localization has made great strides by lev...

Lawrence Schobs, et al. ∙

research

∙ 11/28/2019

Machine Learning for a Low-cost Air Pollution Network

Data collection in economically constrained countries often necessitates...

Michael T. Smith, et al. ∙

research

∙ 06/05/2020

Classification Aware Neural Topic Model and its Application on a New COVID-19 Disinformation Corpus

The explosion of disinformation related to the COVID-19 pandemic has ove...

Xingyi Song, et al. ∙

research

∙ 09/19/2019

Adversarial Vulnerability Bounds for Gaussian Process Classification

Machine learning (ML) classification is increasingly used in safety-crit...

Michael Thomas Smith, et al. ∙

research

∙ 06/21/2019

Variational Bridge Constructs for Grey Box Modelling with Gaussian Processes

This paper introduces a method for inference of heterogeneous dynamical ...

Wil O. C. Ward, et al. ∙

research

∙ 11/06/2018

Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging with Fisher-Discriminative 3D CNN

Cardiac magnetic resonance (CMR) images play a growing role in the diagn...

Le Zhang, et al. ∙

research

∙ 04/22/2020

Gaussian Process Manifold Interpolation for Probabilistic Atrial Activation Maps and Uncertain Conduction Velocity

In patients with atrial fibrillation, local activation time (LAT) maps a...

Sam Coveney, et al. ∙

research

∙ 09/22/2021

Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation

Current Machine Translation (MT) systems achieve very good results on a ...

Diptesh Kanojia, et al. ∙

research

∙ 01/28/2020

Tri-graph Information Propagation for Polypharmacy Side Effect Prediction

The use of drug combinations often leads to polypharmacy side effects (P...

Hao Xu, et al. ∙

research

∙ 05/04/2022

Modelling calibration uncertainty in networks of environmental sensors

Networks of low-cost sensors are becoming ubiquitous, but often suffer f...

Michael Thomas Smith, et al. ∙

research

∙ 04/11/2015

Antenna Array Signal Processing for Quaternion-Valued Wireless Communication Systems

Quaternion-valued wireless communication systems have been studied in th...

Wei Liu, et al. ∙

research

∙ 05/31/2015

Channel Equalization and Beamforming for Quaternion-Valued Wireless Communication Systems

Quaternion-valued wireless communication systems have been studied in th...

Wei Liu, et al. ∙

research

∙ 03/29/2016

Compressive Sensing Based Design of Sparse Tripole Arrays

This paper considers the problem of designing sparse linear tripole arra...

Matthew Hawes, et al. ∙

research

∙ 02/01/2017

Location and Orientation Optimisation for Spatially Stretched Tripole Arrays Based on Compressive Sensing

The design of sparse spatially stretched tripole arrays is an important ...

Matthew Hawes, et al. ∙

research

∙ 03/19/2014

A Compressive Sensing Based Approach to Sparse Wideband Array Design

Sparse wideband sensor array design for sensor location optimisation is ...

Matthew B. Hawes, et al. ∙

research

∙ 06/10/2016

Automatic Genre and Show Identification of Broadcast Media

Huge amounts of digital videos are being produced and broadcast every da...

Mortaza Doulaty, et al. ∙

research

∙ 11/17/2017

How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models

Machine learning models are vulnerable to adversarial examples: minor, i...

Kathrin Grosse, et al. ∙

research

∙ 12/02/2017

GANGs: Generative Adversarial Network Games

Generative Adversarial Networks (GAN) have become one of the most succes...

Frans A. Oliehoek, et al. ∙

research

∙ 11/20/2017

A generalised framework for detailed classification of swimming paths inside the Morris Water Maze

The Morris Water Maze is commonly used in behavioural neuroscience for t...

Avgoustinos Vouros, et al. ∙

research

∙ 10/12/2017

Is Epicurus the father of Reinforcement Learning?

The Epicurean Philosophy is commonly thought as simplistic and hedonisti...

Eleni Vasilaki, et al. ∙

research

∙ 08/15/2017

Gold Standard Online Debates Summaries and First Experiments Towards Automatic Summarization of Online Debate Data

Usage of online textual media is steadily increasing. Daily, more and mo...

Nattapong Sanchan, et al. ∙

research

∙ 08/15/2017

Automatic Summarization of Online Debates

Debate summarization is one of the novel and challenging research areas ...

Nattapong Sanchan, et al. ∙

research

∙ 10/19/2017

Findings of the Second Shared Task on Multimodal Machine Translation and Multilingual Image Description

We present the results from the second shared task on multimodal machine...

Desmond Elliott, et al. ∙

research

∙ 06/17/2016

Stance Detection with Bidirectional Conditional Encoding

Stance detection is the task of classifying the attitude expressed in a ...

Isabelle Augenstein, et al. ∙

research

∙ 03/15/2016

Turing learning: a metric-free approach to inferring behavior and its application to swarms

We propose Turing Learning, a novel system identification method for inf...

Wei Li, et al. ∙

research

∙ 04/20/2017

SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours

Media is full of false claims. Even Oxford Dictionaries named "post-trut...

Leon Derczynski, et al. ∙

research

∙ 05/27/2017

Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes

Often in machine learning, data are collected as a combination of multip...

Zhenwen Dai, et al. ∙

research

∙ 04/27/2017

Compressive Sensing Approaches for Autonomous Object Detection in Video Sequences

Video analytics requires operating with large amounts of data. Compressi...

Danil Kuzin, et al. ∙

research

∙ 04/27/2017

Structured Sparse Modelling with Hierarchical GP

In this paper a new Bayesian model for sparse linear regression with a s...

Danil Kuzin, et al. ∙

research

∙ 11/16/2016

PCT and Beyond: Towards a Computational Framework for `Intelligent' Communicative Systems

Recent years have witnessed increasing interest in the potential benefit...

Prof. Roger K. Moore, et al. ∙

research

∙ 11/10/2016

A stochastically verifiable autonomous control architecture with reasoning

A new agent architecture called Limited Instruction Set Agent (LISA) is ...

Paolo Izzo, et al. ∙

research

∙ 06/15/2012

General Upper Bounds on the Running Time of Parallel Evolutionary Algorithms

We present a new method for analyzing the running time of parallel evolu...

Jörg Lässig, et al. ∙

research

∙ 07/18/2016

Is spoken language all-or-nothing? Implications for future speech-based human-machine interaction

Recent years have seen significant market penetration for voice-based pe...

Roger K. Moore, et al. ∙

research

∙ 05/07/2017

Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images using Weakly-Supervised Joint Convolutional Sparse Coding

Magnetic Resonance Imaging (MRI) offers high-resolution in vivo imaging ...

Yawen Huang, et al. ∙

research

∙ 11/02/2016

Learning Methods for Dynamic Topic Modeling in Automated Behaviour Analysis

Semi-supervised and unsupervised systems provide operators with invaluab...

Olga Isupova, et al. ∙

research

∙ 06/30/2016

Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model

Unsupervised learning on imbalanced data is challenging because, when gi...

Fariba Yousefi, et al. ∙

research

∙ 06/27/2016

Dynamic Hierarchical Dirichlet Process for Abnormal Behaviour Detection in Video

This paper proposes a novel dynamic Hierarchical Dirichlet Process topic...

Olga Isupova, et al. ∙

research

∙ 06/02/2016

Differentially Private Gaussian Processes

A major challenge for machine learning is increasing the availability of...

Michael Thomas Smith, et al. ∙

research

∙ 09/08/2014

Variational Inference for Uncertainty on the Inputs of Gaussian Process Models

The Gaussian process latent variable model (GP-LVM) provides a flexible ...

Andreas C. Damianou, et al. ∙

research

∙ 11/20/2015

Recurrent Gaussian Processes

We define Recurrent Gaussian Processes (RGP) models, a general family of...

César Lincoln C. Mattos, et al. ∙

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