Zobrazeno 1 - 10
of 409
pro vyhledávání: '"Day, Ben"'
Structure-based drug design involves finding ligand molecules that exhibit structural and chemical complementarity to protein pockets. Deep generative methods have shown promise in proposing novel molecules from scratch (de-novo design), avoiding exh
Externí odkaz:
http://arxiv.org/abs/2111.04107
Autor:
Dudley, Robert, Dodgson, Guy, Common, Stephanie, Ogundimu, Emmanuel, Liley, James, O'Grady, Lucy, Watson, Florence, Gibbs, Christopher, Arnott, Bronia, Fernyhough, Charles, Alderson-Day, Ben, Aynsworth, Charlotte
Publikováno v:
In Journal of Psychiatric Research June 2024 174:289-296
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. We show th
Externí odkaz:
http://arxiv.org/abs/2106.05317
Neural ODE Processes approach the problem of meta-learning for dynamics using a latent variable model, which permits a flexible aggregation of contextual information. This flexibility is inherited from the Neural Process framework and allows the mode
Externí odkaz:
http://arxiv.org/abs/2104.14290
Neural Ordinary Differential Equations (NODEs) use a neural network to model the instantaneous rate of change in the state of a system. However, despite their apparent suitability for dynamics-governed time-series, NODEs present a few disadvantages.
Externí odkaz:
http://arxiv.org/abs/2103.12413
Autor:
Gaudelet, Thomas, Day, Ben, Jamasb, Arian R., Soman, Jyothish, Regep, Cristian, Liu, Gertrude, Hayter, Jeremy B. R., Vickers, Richard, Roberts, Charles, Tang, Jian, Roblin, David, Blundell, Tom L., Bronstein, Michael M., Taylor-King, Jake P.
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
Externí odkaz:
http://arxiv.org/abs/2012.05716
Multi-interaction systems abound in nature, from colloidal suspensions to gene regulatory circuits. These systems can produce complex dynamics and graph neural networks have been proposed as a method to extract underlying interactions and predict how
Externí odkaz:
http://arxiv.org/abs/2009.14593
Neural Processes (NPs) are powerful and flexible models able to incorporate uncertainty when representing stochastic processes, while maintaining a linear time complexity. However, NPs produce a latent description by aggregating independent represent
Externí odkaz:
http://arxiv.org/abs/2009.13895
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. In this paper we extend the Factorised
Externí odkaz:
http://arxiv.org/abs/2006.13666
Neural Ordinary Differential Equations (NODEs) are a new class of models that transform data continuously through infinite-depth architectures. The continuous nature of NODEs has made them particularly suitable for learning the dynamics of complex ph
Externí odkaz:
http://arxiv.org/abs/2006.07220