The Alan Turing Institute

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The Alan Turing Institute is the United Kingdom's national institute for data science and artificial intelligence, founded in 2015.

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...
07/30/2020 ∙ by Harrison Wilde, et al. ∙ 21 ∙ share

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

This paper demonstrates the application of Bayesian Artificial Neural Ne...
05/26/2020 ∙ by David K. E. Green, et al. ∙ 20 ∙ share

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Model inference for Ordinary Differential Equations by parametric polynomial kernel regression

Model inference for dynamical systems aims to estimate the future behavi...
08/06/2019 ∙ by David K. E. Green, et al. ∙ 12 ∙ share

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Adequate and fair explanations

Explaining sophisticated machine-learning based systems is an important ...
01/21/2020 ∙ by Nicholas Asher, et al. ∙ 11 ∙ share

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Regularising Deep Networks with DGMs

Here we develop a new method for regularising neural networks where we l...
09/25/2019 ∙ by Matthew Willetts, et al. ∙ 10 ∙ share

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A Model to Search for Synthesizable Molecules

Deep generative models are able to suggest new organic molecules by gene...
06/12/2019 ∙ by John Bradshaw, et al. ∙ 7 ∙ share

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Scalable Partial Explainability in Neural Networks via Flexible Activation Functions

Achieving transparency in black-box deep learning algorithms is still an...
06/10/2020 ∙ by Schyler C. Sun, et al. ∙ 6 ∙ share

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Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders

In clustering we normally output one cluster variable for each datapoint...
09/25/2019 ∙ by Matthew Willetts, et al. ∙ 4 ∙ share

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An Introduction to Probabilistic Programming

This document is designed to be a first-year graduate-level introduction...
09/27/2018 ∙ by Jan-Willem van de Meent, et al. ∙ 2 ∙ share

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Robust Variational Autoencoders for Outlier Detection in Mixed-Type Data

We focus on the problem of unsupervised cell outlier detection in mixed ...
07/15/2019 ∙ by Simão Eduardo, et al. ∙ 2 ∙ share

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On the Constrained Least-cost Tour Problem

We introduce the Constrained Least-cost Tour (CLT) problem: given an und...
06/18/2019 ∙ by Patrick O'Hara, et al. ∙ 1 ∙ share

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Learning a Generative Model for Validity in Complex Discrete Structures

Deep generative models have been successfully used to learn representati...
12/05/2017 ∙ by David Janz, et al. ∙ 0 ∙ share

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On denoising modulo 1 samples of a function

Consider an unknown smooth function f: [0,1] →R, and say we are given n ...
10/27/2017 ∙ by Mihai Cucuringu, et al. ∙ 0 ∙ share

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Learning Disentangled Representations with Semi-Supervised Deep Generative Models

Variational autoencoders (VAEs) learn representations of data by jointly...
06/01/2017 ∙ by N. Siddharth, et al. ∙ 0 ∙ share

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ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network

In recent years, there has been an increasing interest in image-based pl...
09/04/2017 ∙ by Mario Valerio Giuffrida, et al. ∙ 0 ∙ share

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VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning

Deep generative models provide powerful tools for distributions over com...
05/22/2017 ∙ by Akash Srivastava, et al. ∙ 0 ∙ share

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Counterfactual Fairness

Machine learning can impact people with legal or ethical consequences wh...
03/20/2017 ∙ by Matt J. Kusner, et al. ∙ 0 ∙ share

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Grammar Variational Autoencoder

Deep generative models have been wildly successful at learning coherent ...
03/06/2017 ∙ by Matt J. Kusner, et al. ∙ 0 ∙ share

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Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image

We propose a unified formulation for the problem of 3D human pose estima...
01/01/2017 ∙ by Denis Tome, et al. ∙ 0 ∙ share

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Algorithms for Learning Sparse Additive Models with Interactions in High Dimensions

A function f: R^d →R is a Sparse Additive Model (SPAM), if it is of the ...
05/02/2016 ∙ by Hemant Tyagi, et al. ∙ 0 ∙ share

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Kernel Sequential Monte Carlo

We propose kernel sequential Monte Carlo (KSMC), a framework for samplin...
10/11/2015 ∙ by Ingmar Schuster, et al. ∙ 0 ∙ share

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Emo, Love, and God: Making Sense of Urban Dictionary, a Crowd-Sourced Online Dictionary

The Internet facilitates large-scale collaborative projects. The emergen...
12/22/2017 ∙ by Dong Nguyen, et al. ∙ 0 ∙ share

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Worst-case Optimal Submodular Extensions for Marginal Estimation

Submodular extensions of an energy function can be used to efficiently c...
01/10/2018 ∙ by Pankaj Pansari, et al. ∙ 0 ∙ share

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Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping

Consider an unknown smooth function f: [0,1]^d →R, and say we are given ...
03/09/2018 ∙ by Mihai Cucuringu, et al. ∙ 0 ∙ share

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Hierarchical Disentangled Representations

Deep latent-variable models learn representations of high-dimensional da...
04/06/2018 ∙ by Babak Esmaeili, et al. ∙ 0 ∙ share

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Exploring Hyper-Parameter Optimization for Neural Machine Translation on GPU Architectures

Neural machine translation (NMT) has been accelerated by deep learning n...
05/05/2018 ∙ by Robert Lim, et al. ∙ 0 ∙ share

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Predicting Electron Paths

Chemical reactions can be described as the stepwise redistribution of el...
05/23/2018 ∙ by John Bradshaw, et al. ∙ 0 ∙ share

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TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service

Machine learning methods are widely used for a variety of prediction pro...
06/09/2018 ∙ by Amartya Sanyal, et al. ∙ 0 ∙ share

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Towards an understanding of CNNs: analysing the recovery of activation pathways via Deep Convolutional Sparse Coding

Deep Convolutional Sparse Coding (D-CSC) is a framework reminiscent of d...
06/26/2018 ∙ by Michael Murray, et al. ∙ 0 ∙ share

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A diachronic study of historiography

The humanities are often characterized by sociologists as having a low m...
07/04/2018 ∙ by Giovanni Colavizza, et al. ∙ 0 ∙ share

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Multi-kernel unmixing and super-resolution using the Modified Matrix Pencil method

Consider L groups of point sources or spike trains, with the l^th group ...
07/08/2018 ∙ by Stéphane Chrétien, et al. ∙ 0 ∙ share

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Handling Incomplete Heterogeneous Data using VAEs

Variational autoencoders (VAEs), as well as other generative models, hav...
07/10/2018 ∙ by Alfredo Nazabal, et al. ∙ 0 ∙ share

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Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum

Modelling user voting intention in social media is an important research...
08/26/2018 ∙ by Adam Tsakalidis, et al. ∙ 0 ∙ share

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A Modern Retrospective on Probabilistic Numerics

This article attempts to cast the emergence of probabilistic numerics as...
01/14/2019 ∙ by C. J. Oates, et al. ∙ 0 ∙ share

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Efficient Search for Diverse Coherent Explanations

This paper proposes new search algorithms for counterfactual explanation...
01/02/2019 ∙ by Chris Russell, et al. ∙ 0 ∙ share

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The Privacy Blanket of the Shuffle Model

This work studies differential privacy in the context of the recently pr...
03/07/2019 ∙ by Borja Balle, et al. ∙ 0 ∙ share

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GASC: Genre-Aware Semantic Change for Ancient Greek

Word meaning changes over time, depending on linguistic and extra-lingui...
03/13/2019 ∙ by Valerio Perrone, et al. ∙ 0 ∙ share

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Augmenting correlation structures in spatial data using deep generative models

State-of-the-art deep learning methods have shown a remarkable capacity ...
05/23/2019 ∙ by Konstantin Klemmer, et al. ∙ 0 ∙ share

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A Bayesian Hierarchical Model for Criminal Investigations

Potential violent criminals will often need to go through a sequence of ...
07/03/2019 ∙ by F. O. Bunnin, et al. ∙ 0 ∙ share

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QUOTIENT: Two-Party Secure Neural Network Training and Prediction

Recently, there has been a wealth of effort devoted to the design of sec...
07/08/2019 ∙ by Nitin Agrawal, et al. ∙ 0 ∙ share

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Conditional Independence Testing using Generative Adversarial Networks

We consider the hypothesis testing problem of detecting conditional depe...
07/09/2019 ∙ by Alexis Bellot, et al. ∙ 0 ∙ share

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A Robust Two-Sample Test for Time Series data

We develop a general framework for hypothesis testing with time series d...
07/09/2019 ∙ by Alexis Bellot, et al. ∙ 0 ∙ share

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Design choices for productive, secure, data-intensive research at scale in the cloud

We present a policy and process framework for secure environments for pr...
08/23/2019 ∙ by Diego Arenas, et al. ∙ 0 ∙ share

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sktime: A Unified Interface for Machine Learning with Time Series

We present sktime -- a new scikit-learn compatible Python library with a...
09/17/2019 ∙ by Markus Löning, et al. ∙ 0 ∙ share

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Private Protocols for U-Statistics in the Local Model and Beyond

In this paper, we study the problem of computing U-statistics of degree ...
10/09/2019 ∙ by James Bell, et al. ∙ 0 ∙ share

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Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression and Challenge

AI heralds a step-change in the performance and capability of wireless n...
10/11/2019 ∙ by Zhiyong Du, et al. ∙ 0 ∙ share

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Explainable Artificial Intelligence (XAI) for 6G: Improving Trust between Human and Machine

As the 5th Generation (5G) mobile networks are bringing about global soc...
11/11/2019 ∙ by Weisi Guo, et al. ∙ 0 ∙ share

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Data Generation for Neural Programming by Example

Programming by example is the problem of synthesizing a program from a s...
11/06/2019 ∙ by Judith Clymo, et al. ∙ 0 ∙ share

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Graph Input Representations for Machine Learning Applications in Urban Network Analysis

Understanding and learning the characteristics of network paths has been...
12/11/2019 ∙ by Alessio Pagani, et al. ∙ 0 ∙ share

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Soft Random Graphs in Probabilistic Metric Spaces Inter-graph Distance

We present a new method for learning Soft Random Geometric Graphs (SRGGs...
02/03/2020 ∙ by Kangrui Wang, et al. ∙ 0 ∙ share

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