The Alan Turing Institute

research

∙ 07/30/2020

A Recommendation and Risk Classification System for Connecting Rough Sleepers to Essential Outreach Services

Rough sleeping is a chronic problem faced by some of the most disadvanta...

Harrison Wilde, et al. ∙

research

∙ 05/26/2020

Probabilistic solution of chaotic dynamical system inverse problems using Bayesian Artificial Neural Networks

This paper demonstrates the application of Bayesian Artificial Neural Ne...

David K. E. Green, et al. ∙

research

∙ 08/06/2019

Model inference for Ordinary Differential Equations by parametric polynomial kernel regression

Model inference for dynamical systems aims to estimate the future behavi...

David K. E. Green, et al. ∙

research

∙ 04/25/2022

Topological Data Analysis for Anomaly Detection in Host-Based Logs

Topological Data Analysis (TDA) gives practioners the ability to analyse...

Thomas Davies, et al. ∙

research

∙ 01/21/2020

Adequate and fair explanations

Explaining sophisticated machine-learning based systems is an important ...

Nicholas Asher, et al. ∙

research

∙ 09/08/2021

Conservative Policy Construction Using Variational Autoencoders for Logged Data with Missing Values

In high-stakes applications of data-driven decision making like healthca...

Mahed Abroshan, et al. ∙

research

∙ 09/25/2019

Regularising Deep Networks with DGMs

Here we develop a new method for regularising neural networks where we l...

Matthew Willetts, et al. ∙

research

∙ 01/13/2022

Density Estimation from Schlieren Images through Machine Learning

This study proposes a radically alternate approach for extracting quanti...

Bryn Noel Ubald, et al. ∙

research

∙ 02/16/2022

A Review of Topological Data Analysis for Cybersecurity

In cybersecurity it is often the case that malicious or anomalous activi...

Thomas Davies, et al. ∙

research

∙ 06/12/2019

A Model to Search for Synthesizable Molecules

Deep generative models are able to suggest new organic molecules by gene...

John Bradshaw, et al. ∙

research

∙ 05/13/2022

ImageSig: A signature transform for ultra-lightweight image recognition

This paper introduces a new lightweight method for image recognition. Im...

Mohamed R. Ibrahim, et al. ∙

research

∙ 06/10/2020

Scalable Partial Explainability in Neural Networks via Flexible Activation Functions

Achieving transparency in black-box deep learning algorithms is still an...

Schyler C. Sun, et al. ∙

research

∙ 09/25/2019

Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders

In clustering we normally output one cluster variable for each datapoint...

Matthew Willetts, et al. ∙

research

∙ 09/27/2018

An Introduction to Probabilistic Programming

This document is designed to be a first-year graduate-level introduction...

Jan-Willem van de Meent, et al. ∙

research

∙ 07/15/2019

Robust Variational Autoencoders for Outlier Detection in Mixed-Type Data

We focus on the problem of unsupervised cell outlier detection in mixed ...

Simão Eduardo, et al. ∙

research

∙ 04/29/2022

Handling and Presenting Harmful Text

Textual data can pose a risk of serious harm. These harms can be categor...

Leon Derczynski, et al. ∙

research

∙ 06/18/2019

On the Constrained Least-cost Tour Problem

We introduce the Constrained Least-cost Tour (CLT) problem: given an und...

Patrick O'Hara, et al. ∙

research

∙ 11/02/2021

AI Ethics Statements – Analysis and lessons learnt from NeurIPS Broader Impact Statements

Ethics statements have been proposed as a mechanism to increase transpar...

Carolyn Ashurst, et al. ∙

research

∙ 05/15/2020

Reliable Local Explanations for Machine Listening

One way to analyse the behaviour of machine learning models is through l...

Saumitra Mishra, et al. ∙

research

∙ 09/09/2022

Affinity-VAE for disentanglement, clustering and classification of objects in multidimensional image data

In this work we present affinity-VAE: a framework for automatic clusteri...

Jola Mirecka, et al. ∙

research

∙ 06/28/2020

Deep Orthogonal Decompositions for Convective Nowcasting

Near-term prediction of the structured spatio-temporal processes driving...

Daniel J. Tait, et al. ∙

research

∙ 01/20/2023

Towards Openness Beyond Open Access: User Journeys through 3 Open AI Collaboratives

Open Artificial Intelligence (Open source AI) collaboratives offer alter...

Jennifer Ding, et al. ∙

research

∙ 06/28/2020

Variational Autoencoding of PDE Inverse Problems

Specifying a governing physical model in the presence of missing physics...

Daniel J. Tait, et al. ∙

research

∙ 07/14/2020

Explicit Regularisation in Gaussian Noise Injections

We study the regularisation induced in neural networks by Gaussian noise...

Alexander Camuto, et al. ∙

research

∙ 07/22/2020

Better Early than Late: Fusing Topics with Word Embeddings for Neural Question Paraphrase Identification

Question paraphrase identification is a key task in Community Question A...

Nicole Peinelt, et al. ∙

research

∙ 07/22/2020

SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection

Lexical Semantic Change detection, i.e., the task of identifying words t...

Dominik Schlechtweg, et al. ∙

research

∙ 07/23/2020

Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation

Non-intrusive reduced-order models (ROMs) have recently generated consid...

Romit Maulik, et al. ∙

research

∙ 07/23/2020

MLJ: A Julia package for composable Machine Learning

MLJ (Machine Learing in Julia) is an open source software package provid...

Anthony D. Blaom, et al. ∙

research

∙ 12/16/2022

Robust Explanation Constraints for Neural Networks

Post-hoc explanation methods are used with the intent of providing insig...

Matthew Wicker, et al. ∙

research

∙ 12/08/2022

A probabilistic peridynamic framework with an application to the study of the statistical size effect

Mathematical models are essential for understanding and making predictio...

Mark Hobbs, et al. ∙

research

∙ 10/07/2020

Data Driven Density Functional Theory: A case for Physics Informed Learning

We propose a novel data-driven approach to solving a classical statistic...

Peter Yatsyshin, et al. ∙

research

∙ 11/19/2020

Continuous calibration of a digital twin: comparison of particle filter and Bayesian calibration approaches

Assimilation of continuously streamed monitored data is an essential com...

Rebecca Ward, et al. ∙

research

∙ 06/08/2021

Curriculum Design for Teaching via Demonstrations: Theory and Applications

We consider the problem of teaching via demonstrations in sequential dec...

Gaurav Yengera, et al. ∙

research

∙ 07/15/2021

From Reddit to Wall Street: The role of committed minorities in financial collective action

In January 2021, retail investors coordinated on Reddit to target short ...

Lorenzo Lucchini, et al. ∙

research

∙ 11/18/2022

Active Learning with Convolutional Gaussian Neural Processes for Environmental Sensor Placement

Deploying environmental measurement stations can be a costly and time-co...

Tom R. Andersson, et al. ∙

research

∙ 11/12/2022

TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data

Personal data collected at scale promises to improve decision-making and...

Florimond Houssiau, et al. ∙

research

∙ 09/15/2021

An influencer-based approach to understanding radical right viral tweets

Radical right influencers routinely use social media to spread highly di...

Laila Sprejer, et al. ∙

research

∙ 11/23/2021

Weight Pruning and Uncertainty in Radio Galaxy Classification

In this work we use variational inference to quantify the degree of epis...

Devina Mohan, et al. ∙

research

∙ 05/06/2022

Synthetic Data – what, why and how?

This explainer document aims to provide an overview of the current state...

James Jordon, et al. ∙

research

∙ 09/17/2022

Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey

Data quality is the key factor for the development of trustworthy AI in ...

Xiaodan Xing, et al. ∙

research

∙ 03/09/2022

Revealing the Excitation Causality between Climate and Political Violence via a Neural Forward-Intensity Poisson Process

The causal mechanism between climate and political violence is fraught w...

Schyler C. Sun, et al. ∙

research

∙ 09/30/2022

Φ-DVAE: Learning Physically Interpretable Representations with Nonlinear Filtering

Incorporating unstructured data into physical models is a challenging pr...

Alex Glyn-Davies, et al. ∙

research

∙ 02/08/2022

Multilevel Delayed Acceptance MCMC

We develop a novel Markov chain Monte Carlo (MCMC) method that exploits ...

Mikkel B. Lykkegaard, et al. ∙

research

∙ 10/04/2022

A Probabilistic Model for Aircraft in Climb using Monotonic Functional Gaussian Process Emulators

Ensuring vertical separation is a key means of maintaining safe separati...

Nick Pepper, et al. ∙

research

∙ 12/10/2021

An Interface between Legacy and Modern Mobile Devices for Digital Identity

In developing regions a substantial number of users rely on legacy and u...

Vasilios Mavroudis, et al. ∙

research

∙ 12/04/2021

Orientation Aware Weapons Detection In Visual Data : A Benchmark Dataset

Automatic detection of weapons is significant for improving security and...

Nazeef Ul Haq, et al. ∙

research

∙ 12/03/2021

Chronological Causal Bandits

This paper studies an instance of the multi-armed bandit (MAB) problem, ...

Neil Dhir, et al. ∙

research

∙ 10/21/2021

Statistical Finite Elements via Langevin Dynamics

The recent statistical finite element method (statFEM) provides a cohere...

Ömer Deniz Akyıldız, et al. ∙

research

∙ 11/08/2021

E(2) Equivariant Self-Attention for Radio Astronomy

In this work we introduce group-equivariant self-attention models to add...

Micah Bowles, et al. ∙

research

∙ 11/08/2021

Can semi-supervised learning reduce the amount of manual labelling required for effective radio galaxy morphology classification?

In this work, we examine the robustness of state-of-the-art semi-supervi...

Inigo V. Slijepcevic, et al. ∙

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