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20232026
most citedExplaining Hypergraph Neural Networks: From Local Explanations to Global Concepts

1 citations · 2 across the 7 of their papers we have counts for

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cs.LG2026

CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning

Dongxia Wu, Shiye Su, Yuhui Zhang +4

Building virtual cells with generative models to simulate cellular behavior in silico is emerging as a promising paradigm for accelerating drug discovery. However, prior image-base…

cs.LG2025

Three Forms of Stochastic Injection for Improved Distribution-to-Distribution Generative Modeling

Shiye Su, Yuhui Zhang, Linqi Zhou +2

Modeling transformations between arbitrary data distributions is a fundamental scientific challenge, arising in applications like drug discovery and evolutionary simulation. While…

cs.LG2025★ 1 cited

OpenThoughts: Data Recipes for Reasoning Models

Etash Guha, Ryan Marten, Sedrick Keh +47

Reasoning models have made rapid progress on many benchmarks involving math, code, and science. Yet, there are still many open questions about the best training recipes for reasoni…

cs.LG2024★ 1 cited

Explaining Hypergraph Neural Networks: From Local Explanations to Global Concepts

Shiye Su, Iulia Duta, Lucie Charlotte Magister +1

Hypergraph neural networks are a class of powerful models that leverage the message passing paradigm to learn over hypergraphs, a generalization of graphs well-suited to describing…

cs.LG2023

ALMANACS: A Simulatability Benchmark for Language Model Explainability

Edmund Mills, Shiye Su, Stuart Russell +1

How do we measure the efficacy of language model explainability methods? While many explainability methods have been developed, they are typically evaluated on bespoke tasks, preve…