Eric Qu

11 papers A* 3Journal 7Unranked 1
YearRankTypeTitle / Venue / Authors
2026 J jnl
A recipe for scalable attention-based MLIPs: unlocking long-range accuracy with all-to-all node attention.
CoRR
Eric Qu, Brandon M. Wood, Aditi S. Krishnapriyan, Zachary W. Ulissi
2026 J jnl
From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures.
CoRR
Ryan Liu, Eric Qu, Tobias Kreiman, Samuel M. Blau, Aditi S. Krishnapriyan
2025 J jnl
Transformers Discover Molecular Structure Without Graph Priors.
CoRR
Tobias Kreiman, Yutong Bai, Fadi Atieh, Elizabeth Weaver, Eric Qu, Aditi S. Krishnapriyan
2024 J jnl
Autoencoding Hyperbolic Representation for Adversarial Generation.
Trans. Mach. Learn. Res.
Eric Qu, Dongmian Zou
2024 A* conf
CNN Kernels Can Be the Best Shapelets.
ICLR
Eric Qu, Yansen Wang, Xufang Luo, Wenqiang He, Kan Ren, Dongsheng Li
2024 — conf
Hyperbolic Kernel Convolution: A Generic Framework.
LoG
Eric Qu, Lige Zhang, Habib Debaya, Yue Wu, Dongmian Zou
2024 A* conf
The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains.
NeurIPS
Eric Qu, Aditi S. Krishnapriyan
2024 J jnl
The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains.
CoRR
Eric Qu, Aditi S. Krishnapriyan
2023 A* conf
Data Continuity Matters: Improving Sequence Modeling with Lipschitz Regularizer.
ICLR
Eric Qu, Xufang Luo, Dongsheng Li
2023 J jnl
Hyperbolic Convolution via Kernel Point Aggregation.
CoRR
Eric Qu, Dongmian Zou
2022 J jnl
Hyperbolic Neural Networks for Molecular Generation.
CoRR
Eric Qu, Dongmian Zou