The University of Sheffield

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A world top-100 university and number one in the UK for student satisfaction in the 2014-15 Times Higher Student Experience Survey.

Unifying Homophily and Heterophily Network Transformation via Motifs

Higher-order proximity (HOP) is fundamental for most network embedding m...
12/21/2020 ∙ by Yan Ge, et al. ∙ 14 ∙ share

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Physically-inspired Gaussian processes for transcriptional regulation in Drosophila melanogaster

The regulatory process in Drosophila melanogaster is thoroughly studied ...
08/29/2018 ∙ by Andrés F. López-Lopera, et al. ∙ 12 ∙ share

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Differentially Private Regression and Classification with Sparse Gaussian Processes

A continuing challenge for machine learning is providing methods to perf...
09/19/2019 ∙ by Michael Thomas Smith, et al. ∙ 12 ∙ share

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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

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Non-linear process convolutions for multi-output Gaussian processes

The paper introduces a non-linear version of the process convolution for...
10/10/2018 ∙ by Mauricio A. Álvarez, et al. ∙ 10 ∙ share

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Machine Learning for a Low-cost Air Pollution Network

Data collection in economically constrained countries often necessitates...
11/28/2019 ∙ by Michael T. Smith, et al. ∙ 9 ∙ share

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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...
06/05/2020 ∙ by Xingyi Song, et al. ∙ 8 ∙ share

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Adversarial Vulnerability Bounds for Gaussian Process Classification

Machine learning (ML) classification is increasingly used in safety-crit...
09/19/2019 ∙ by Michael Thomas Smith, et al. ∙ 6 ∙ share

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Variational Bridge Constructs for Grey Box Modelling with Gaussian Processes

This paper introduces a method for inference of heterogeneous dynamical ...
06/21/2019 ∙ by Wil O. C. Ward, et al. ∙ 5 ∙ share

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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...
11/06/2018 ∙ by Le Zhang, et al. ∙ 4 ∙ share

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

In patients with atrial fibrillation, local activation time (LAT) maps a...
04/22/2020 ∙ by Sam Coveney, et al. ∙ 4 ∙ share

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Tri-graph Information Propagation for Polypharmacy Side Effect Prediction

The use of drug combinations often leads to polypharmacy side effects (P...
01/28/2020 ∙ by Hao Xu, et al. ∙ 1 ∙ share

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Antenna Array Signal Processing for Quaternion-Valued Wireless Communication Systems

Quaternion-valued wireless communication systems have been studied in th...
04/11/2015 ∙ by Wei Liu, et al. ∙ 0 ∙ share

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Channel Equalization and Beamforming for Quaternion-Valued Wireless Communication Systems

Quaternion-valued wireless communication systems have been studied in th...
05/31/2015 ∙ by Wei Liu, et al. ∙ 0 ∙ share

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Compressive Sensing Based Design of Sparse Tripole Arrays

This paper considers the problem of designing sparse linear tripole arra...
03/29/2016 ∙ by Matthew Hawes, et al. ∙ 0 ∙ share

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

The design of sparse spatially stretched tripole arrays is an important ...
02/01/2017 ∙ by Matthew Hawes, et al. ∙ 0 ∙ share

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A Compressive Sensing Based Approach to Sparse Wideband Array Design

Sparse wideband sensor array design for sensor location optimisation is ...
03/19/2014 ∙ by Matthew B. Hawes, et al. ∙ 0 ∙ share

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Automatic Genre and Show Identification of Broadcast Media

Huge amounts of digital videos are being produced and broadcast every da...
06/10/2016 ∙ by Mortaza Doulaty, et al. ∙ 0 ∙ share

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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...
11/17/2017 ∙ by Kathrin Grosse, et al. ∙ 0 ∙ share

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GANGs: Generative Adversarial Network Games

Generative Adversarial Networks (GAN) have become one of the most succes...
12/02/2017 ∙ by Frans A. Oliehoek, et al. ∙ 0 ∙ share

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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...
11/20/2017 ∙ by Avgoustinos Vouros, et al. ∙ 0 ∙ share

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Is Epicurus the father of Reinforcement Learning?

The Epicurean Philosophy is commonly thought as simplistic and hedonisti...
10/12/2017 ∙ by Eleni Vasilaki, et al. ∙ 0 ∙ share

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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...
08/15/2017 ∙ by Nattapong Sanchan, et al. ∙ 0 ∙ share

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Automatic Summarization of Online Debates

Debate summarization is one of the novel and challenging research areas ...
08/15/2017 ∙ by Nattapong Sanchan, et al. ∙ 0 ∙ share

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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...
10/19/2017 ∙ by Desmond Elliott, et al. ∙ 0 ∙ share

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Stance Detection with Bidirectional Conditional Encoding

Stance detection is the task of classifying the attitude expressed in a ...
06/17/2016 ∙ by Isabelle Augenstein, et al. ∙ 0 ∙ share

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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...
03/15/2016 ∙ by Wei Li, et al. ∙ 0 ∙ share

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

Media is full of false claims. Even Oxford Dictionaries named "post-trut...
04/20/2017 ∙ by Leon Derczynski, et al. ∙ 0 ∙ share

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Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes

Often in machine learning, data are collected as a combination of multip...
05/27/2017 ∙ by Zhenwen Dai, et al. ∙ 0 ∙ share

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Compressive Sensing Approaches for Autonomous Object Detection in Video Sequences

Video analytics requires operating with large amounts of data. Compressi...
04/27/2017 ∙ by Danil Kuzin, et al. ∙ 0 ∙ share

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Structured Sparse Modelling with Hierarchical GP

In this paper a new Bayesian model for sparse linear regression with a s...
04/27/2017 ∙ by Danil Kuzin, et al. ∙ 0 ∙ share

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

Recent years have witnessed increasing interest in the potential benefit...
11/16/2016 ∙ by Prof. Roger K. Moore, et al. ∙ 0 ∙ share

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A stochastically verifiable autonomous control architecture with reasoning

A new agent architecture called Limited Instruction Set Agent (LISA) is ...
11/10/2016 ∙ by Paolo Izzo, et al. ∙ 0 ∙ share

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General Upper Bounds on the Running Time of Parallel Evolutionary Algorithms

We present a new method for analyzing the running time of parallel evolu...
06/15/2012 ∙ by Jörg Lässig, et al. ∙ 0 ∙ share

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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...
07/18/2016 ∙ by Roger K. Moore, et al. ∙ 0 ∙ share

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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 ...
05/07/2017 ∙ by Yawen Huang, et al. ∙ 0 ∙ share

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Learning Methods for Dynamic Topic Modeling in Automated Behaviour Analysis

Semi-supervised and unsupervised systems provide operators with invaluab...
11/02/2016 ∙ by Olga Isupova, et al. ∙ 0 ∙ share

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Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model

Unsupervised learning on imbalanced data is challenging because, when gi...
06/30/2016 ∙ by Fariba Yousefi, et al. ∙ 0 ∙ share

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Dynamic Hierarchical Dirichlet Process for Abnormal Behaviour Detection in Video

This paper proposes a novel dynamic Hierarchical Dirichlet Process topic...
06/27/2016 ∙ by Olga Isupova, et al. ∙ 0 ∙ share

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Differentially Private Gaussian Processes

A major challenge for machine learning is increasing the availability of...
06/02/2016 ∙ by Michael Thomas Smith, et al. ∙ 0 ∙ share

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Variational Inference for Uncertainty on the Inputs of Gaussian Process Models

The Gaussian process latent variable model (GP-LVM) provides a flexible ...
09/08/2014 ∙ by Andreas C. Damianou, et al. ∙ 0 ∙ share

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Recurrent Gaussian Processes

We define Recurrent Gaussian Processes (RGP) models, a general family of...
11/20/2015 ∙ by César Lincoln C. Mattos, et al. ∙ 0 ∙ share

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Variational Auto-encoded Deep Gaussian Processes

We develop a scalable deep non-parametric generative model by augmenting...
11/19/2015 ∙ by Zhenwen Dai, et al. ∙ 0 ∙ share

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GLASSES: Relieving The Myopia Of Bayesian Optimisation

We present GLASSES: Global optimisation with Look-Ahead through Stochast...
10/21/2015 ∙ by Javier Gonzalez, et al. ∙ 0 ∙ share

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A Bayesian Compressed Sensing Kalman Filter for Direction of Arrival Estimation

In this paper, we look to address the problem of estimating the dynamic ...
09/21/2015 ∙ by Matthew Hawes, et al. ∙ 0 ∙ share

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MCMC for Variationally Sparse Gaussian Processes

Gaussian process (GP) models form a core part of probabilistic machine l...
06/12/2015 ∙ by James Hensman, et al. ∙ 0 ∙ share

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Spike and Slab Gaussian Process Latent Variable Models

The Gaussian process latent variable model (GP-LVM) is a popular approac...
05/10/2015 ∙ by Zhenwen Dai, et al. ∙ 0 ∙ share

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Bayesian Optimization for Synthetic Gene Design

We address the problem of synthetic gene design using Bayesian optimizat...
05/07/2015 ∙ by Javier Gonzalez, et al. ∙ 0 ∙ share

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Gaussian Process Pseudo-Likelihood Models for Sequence Labeling

Several machine learning problems arising in natural language processing...
12/25/2014 ∙ by P. K. Srijith, et al. ∙ 0 ∙ share

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Generalized Canonical Correlation Analysis and Its Application to Blind Source Separation Based on a Dual-Linear Predictor Structure

Blind source separation (BSS) is one of the most important and establish...
03/09/2014 ∙ by Wei Liu, et al. ∙ 0 ∙ share

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