Search Results for author: Ben Day

Found 16 papers, 9 papers with code

Structure-aware generation of drug-like molecules

no code implementations7 Nov 2021 Pavol Drotár, Arian Rokkum Jamasb, Ben Day, Cătălina Cangea, Pietro Liò

Molecules are built atom-by-atom inside pockets, guided by structural information from crystallographic data.

On Second Order Behaviour in Augmented Neural ODEs: A Short Summary

no code implementations NeurIPS Workshop DLDE 2021 Alexander Luke Ian Norcliffe, Cristian Bodnar, Ben Day, Nikola Simidjievski, Pietro Lio

In Norcliffe et al.[13], we discussed and systematically analysed how Neural ODEs (NODEs) can learn higher-order order dynamics.

Neural ODE Processes: A Short Summary

1 code implementation NeurIPS Workshop DLDE 2021 Alexander Luke Ian Norcliffe, Cristian Bodnar, Ben Day, Jacob Moss, Pietro Lio

To this end, we introduce Neural ODE Processes (NDPs), a new class of stochastic processes determined by a distribution over Neural ODEs.

Time Series Time Series Analysis

Attentional Meta-learners for Few-shot Polythetic Classification

1 code implementation9 Jun 2021 Ben Day, Ramon Viñas, Nikola Simidjievski, Pietro Liò

Polythetic classifications, based on shared patterns of features that need neither be universal nor constant among members of a class, are common in the natural world and greatly outnumber monothetic classifications over a set of features.

Classification feature selection +1

Meta-learning using privileged information for dynamics

1 code implementation ICLR Workshop Learning_to_Learn 2021 Ben Day, Alexander Norcliffe, Jacob Moss, Pietro Liò

Neural ODE Processes approach the problem of meta-learning for dynamics using a latent variable model, which permits a flexible aggregation of contextual information.

Meta-Learning

Neural ODE Processes

2 code implementations ICLR 2021 Alexander Norcliffe, Cristian Bodnar, Ben Day, Jacob Moss, Pietro Liò

To address these problems, we introduce Neural ODE Processes (NDPs), a new class of stochastic processes determined by a distribution over Neural ODEs.

Time Series Time Series Analysis

Utilising Graph Machine Learning within Drug Discovery and Development

no code implementations9 Dec 2020 Thomas Gaudelet, Ben Day, Arian R. Jamasb, Jyothish Soman, Cristian Regep, Gertrude Liu, Jeremy B. R. Hayter, Richard Vickers, Charles Roberts, Jian Tang, David Roblin, Tom L. Blundell, Michael M. Bronstein, Jake P. Taylor-King

Graph Machine Learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets - amongst other data types.

BIG-bench Machine Learning Drug Discovery

The Role of Isomorphism Classes in Multi-Relational Datasets

no code implementations30 Sep 2020 Vijja Wichitwechkarn, Ben Day, Cristian Bodnar, Matthew Wales, Pietro Liò

The current training and evaluation procedures for these models through the use of synthetic multi-relational datasets however are agnostic to interaction network isomorphism classes, which produce identical dynamics up to initial conditions.

Message Passing Neural Processes

no code implementations29 Sep 2020 Ben Day, Cătălina Cangea, Arian R. Jamasb, Pietro Liò

Neural Processes (NPs) are powerful and flexible models able to incorporate uncertainty when representing stochastic processes, while maintaining a linear time complexity.

Few-Shot Learning

Uncertainty in Neural Relational Inference Trajectory Reconstruction

1 code implementation24 Jun 2020 Vasileios Karavias, Ben Day, Pietro Liò

Neural networks used for multi-interaction trajectory reconstruction lack the ability to estimate the uncertainty in their outputs, which would be useful to better analyse and understand the systems they model.

On Second Order Behaviour in Augmented Neural ODEs

1 code implementation NeurIPS 2020 Alexander Norcliffe, Cristian Bodnar, Ben Day, Nikola Simidjievski, Pietro Liò

Neural Ordinary Differential Equations (NODEs) are a new class of models that transform data continuously through infinite-depth architectures.

Image Classification

Proximal Distilled Evolutionary Reinforcement Learning

1 code implementation24 Jun 2019 Cristian Bodnar, Ben Day, Pietro Lió

We propose a novel algorithm called Proximal Distilled Evolutionary Reinforcement Learning (PDERL) that is characterised by a hierarchical integration between evolution and learning.

OpenAI Gym reinforcement-learning +1

Factorised Neural Relational Inference for Multi-Interaction Systems

2 code implementations21 May 2019 Ezra Webb, Ben Day, Helena Andres-Terre, Pietro Lió

Many complex natural and cultural phenomena are well modelled by systems of simple interactions between particles.

Trajectory Prediction

On Graph Classification Networks, Datasets and Baselines

no code implementations12 May 2019 Enxhell Luzhnica, Ben Day, Pietro Liò

Graph classification receives a great deal of attention from the non-Euclidean machine learning community.

BIG-bench Machine Learning General Classification +1

Introducing Curvature to the Label Space

no code implementations22 Oct 2018 Conor Sheehan, Ben Day, Pietro Liò

One-hot encoding is a labelling system that embeds classes as standard basis vectors in a label space.

General Classification

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