Bryan Perozzi
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Scaling Large Language Models For Next-Generation Single-Cell Analysis
Syed Asad Rizvi
Daniel Levine
Aakash Patel
Shiyang Zhang
Eric Wang
Curtis Jamison Perry
Nicole Mayerli Constante
Sizhuang He
David Zhang
Cerise Tang
Zhuoyang Lyu
Rayyan Darji
Chang Li
Emily Sun
David Jeong
Lawrence Zhao
Jennifer Kwan
David Braun
Brian Hafler
Hattie Chung
Rahul M. Dhodapkar
Paul Jaeger
Jeffrey Ishizuka
David van Dijk
biorxiv (2025)
Position Paper: Graph Learning Loses Relevance Due To Poor Benchmarks
Maya Bechler-Speicher
Ben Finkelshtein
Fabrizio Frasca
Luis Müller
Jan Tönshoff
Antoine Siraudin
Viktor Zaverkin
Michael Bronstein
Mathias Niepert
Michael Galkin
Christopher Morris
2025
General Geospatial Inference with a Population Dynamics Foundation Model
Chaitanya Kamath
Prithul Sarker
Joydeep Paul
Yael Mayer
Sheila de Guia
Jamie McPike
Adam Boulanger
David Schottlander
Yao Xiao
Manjit Chakravarthy Manukonda
Monica Bharel
Von Nguyen
Luke Barrington
Niv Efron
Krish Eswaran
Shravya Shetty
arXiv (2024)
Unsupervised Embedding Quality Evaluation
Marina Munkhoeva
Topology, Algebra, and Geometry in Machine Learning (2023)
HUGE: Huge Unsupervised Graph Embeddings with TPUs
SIGKDD Conference on Knowledge Discovery and Data Mining, ACM (2023) (to appear)
Tackling Provably Hard Representative Selection via Graph Neural Networks
Transactions on Machine Learning Research (2023)
TpuGraphs: Performance Prediction Datasets on Large Tensor Computational Graphs
Mangpo Phothilimthana
Kaidi Cao
Charith Mendis
Advances in Neural Information Processing Systems (2023)
SubMix: Learning to Mix Graph Sampling Heuristics
Josh Dillon
Johannes Gasteiger
2023
