7 citations · 22 across the 10 of their papers we have counts for
10 papers
When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff
Paul Scherer, Andreas Kirsch, Jake P. Taylor-King
Real-world experimental scenarios are characterized by the presence of heteroskedastic aleatoric uncertainty, and this uncertainty can be correlated in batched settings. The bias--…
Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery
Yana Lishkova, Paul Scherer, Steffen Ridderbusch +4
By one of the most fundamental principles in physics, a dynamical system will exhibit those motions which extremise an action functional. This leads to the formation of the Euler-L…
Distributed representations of graphs for drug pair scoring
Paul Scherer, Pietro Liò, Mateja Jamnik
In this paper we study the practicality and usefulness of incorporating distributed representations of graphs into models within the context of drug pair scoring. We argue that the…
PyRelationAL: a python library for active learning research and development
Paul Scherer, Alison Pouplin, Alice Del Vecchio +6
Active learning (AL) is a sub-field of ML focused on the development of methods to iteratively and economically acquire data by strategically querying new data points that are the…
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
Benedek Rozemberczki, Paul Scherer, Yixuan He +8
We present PyTorch Geometric Temporal a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing. The main goal of…
Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks
Benedek Rozemberczki, Paul Scherer, Oliver Kiss +2
Recurrent graph convolutional neural networks are highly effective machine learning techniques for spatiotemporal signal processing. Newly proposed graph neural network architectur…