Eric Qu

According to our database, Eric Qu authored at least 10 papers between 2022 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

A recipe for scalable attention-based MLIPs: unlocking long-range accuracy with all-to-all node attention.

Eric Qu, Brandon M. Wood, Aditi S. Krishnapriyan, Zachary W. Ulissi

CoRR, March, 2026

From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures.

Ryan Liu, Eric Qu, Tobias Kreiman, Samuel M. Blau, Aditi S. Krishnapriyan

CoRR, February, 2026

2025

Transformers Discover Molecular Structure Without Graph Priors.

Tobias Kreiman, Yutong Bai, Fadi Atieh, Elizabeth Weaver, Eric Qu, Aditi S. Krishnapriyan

CoRR, October, 2025

2024

Autoencoding Hyperbolic Representation for Adversarial Generation.

Eric Qu, Dongmian Zou

Trans. Mach. Learn. Res., 2024

The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains.

Eric Qu, Aditi S. Krishnapriyan

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Hyperbolic Kernel Convolution: A Generic Framework.

Eric Qu, Lige Zhang, Habib Debaya, Yue Wu, Dongmian Zou

Proceedings of the Learning on Graphs Conference, 26-29 November 2024, Virtual., 2024

CNN Kernels Can Be the Best Shapelets.

Eric Qu, Yansen Wang, Xufang Luo, Wenqiang He, Kan Ren, Dongsheng Li

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Hyperbolic Convolution via Kernel Point Aggregation.

Eric Qu, Dongmian Zou

CoRR, 2023

Data Continuity Matters: Improving Sequence Modeling with Lipschitz Regularizer.

Eric Qu, Xufang Luo, Dongsheng Li

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Hyperbolic Neural Networks for Molecular Generation.

Eric Qu, Dongmian Zou

CoRR, 2022