5 citations · 7 across the 4 of their papers we have counts for
4 papers
Privacy-Preserving Gaussian Process Regression -- A Modular Approach to the Application of Homomorphic Encryption
Peter Fenner, Edward O. Pyzer-Knapp
Much of machine learning relies on the use of large amounts of data to train models to make predictions. When this data comes from multiple sources, for example when evaluation of…
Fully Bayesian Recurrent Neural Networks for Safe Reinforcement Learning
Matt Benatan, Edward O. Pyzer-Knapp
Reinforcement Learning (RL) has demonstrated state-of-the-art results in a number of autonomous system applications, however many of the underlying algorithms rely on black-box pre…
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development
Laura-Jayne Gardiner, Anna Paola Carrieri, Jenny Wilshaw +3
Biomedical data, particularly in the field of genomics, has characteristics which make it challenging for machine learning applications - it can be sparse, high dimensional and noi…
Space-Filling Curves as a Novel Crystal Structure Representation for Machine Learning Models
Dipti Jasrasaria, Edward O. Pyzer-Knapp, Dmitrij Rappoport +1
A fundamental problem in applying machine learning techniques for chemical problems is to find suitable representations for molecular and crystal structures. While the structure re…