20 citations · 61 across the 12 of their papers we have counts for
13 papers
Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry
Hatem Helal, Jesun Firoz, Jenna Bilbrey +5
Molecular property calculations are the bedrock of chemical physics. High-fidelity \textit{ab initio} modeling techniques for computing the molecular properties can be prohibitivel…
Tuple Packing: Efficient Batching of Small Graphs in Graph Neural Networks
Mario Michael Krell, Manuel Lopez, Sreenidhi Anand +2
When processing a batch of graphs in machine learning models such as Graph Neural Networks (GNN), it is common to combine several small graphs into one overall graph to accelerate…
Classifier Transfer with Data Selection Strategies for Online Support Vector Machine Classification with Class Imbalance
Mario Michael Krell, Nils Wilshusen, Anett Seeland +1
Objective: Classifier transfers usually come with dataset shifts. To overcome them, online strategies have to be applied. For practical applications, limitations in the computation…
NanoBatch Privacy: Enabling fast Differentially Private learning on the IPU
Edward H. Lee, Mario Michael Krell, Alexander Tsyplikhin +3
Differentially private SGD (DPSGD) has recently shown promise in deep learning. However, compared to non-private SGD, the DPSGD algorithm places computational overheads that can un…
OrigamiSet1.0: Two New Datasets for Origami Classification and Difficulty Estimation
Daniel Ma, Gerald Friedland, Mario Michael Krell
Origami is becoming more and more relevant to research. However, there is no public dataset yet available and there hasn't been any research on this topic in machine learning. We c…
Hardware-accelerated Simulation-based Inference of Stochastic Epidemiology Models for COVID-19
Sourabh Kulkarni, Mario Michael Krell, Seth Nabarro +1
Epidemiology models are central in understanding and controlling large scale pandemics. Several epidemiology models require simulation-based inference such as Approximate Bayesian…