92 citations · 612 across the 42 of their papers we have counts for
5 papers · 1 filter
Classical symmetries and the Quantum Approximate Optimization Algorithm
Ruslan Shaydulin, Stuart Hadfield, Tad Hogg +1
We study the relationship between the Quantum Approximate Optimization Algorithm (QAOA) and the underlying symmetries of the objective function to be optimized. Our approach formal…
Recent Advances in Scalable Network Generation
Manuel Penschuck, Ulrik Brandes, Michael Hamann +5
Random graph models are frequently used as a controllable and versatile data source for experimental campaigns in various research fields. Generating such data-sets at scale is a n…
Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximization
Fei Ding, Xiaohong Zhang, Justin Sybrandt +1
Graph representation learning based on graph neural networks (GNNs) can greatly improve the performance of downstream tasks, such as node and graph classification. However, the gen…
CBAG: Conditional Biomedical Abstract Generation
Justin Sybrandt, Ilya Safro
Biomedical research papers use significantly different language and jargon when compared to typical English text, which reduces the utility of pre-trained NLP models in this domain…
AGATHA: Automatic Graph-mining And Transformer based Hypothesis generation Approach
Justin Sybrandt, Ilya Tyagin, Michael Shtutman +1
Medical research is risky and expensive. Drug discovery, as an example, requires that researchers efficiently winnow thousands of potential targets to a small candidate set for mor…