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20212025
most citedRethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective

12 citations · 20 across the 5 of their papers we have counts for

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5 papers

physics.chem-ph2025★ 1 cited

Spin-Adapted Neural Network Wavefunctions in Real Space

Ruichen Li, Yuzhi Liu, Du Jiang +7

Spin plays a fundamental role in understanding electronic structure, yet many real-space wavefunction methods fail to adequately consider it. We introduce the Spin-Adapted Antisymm…

physics.chem-ph2025

Neural Scaling Laws Surpass Chemical Accuracy for the Many-Electron Schrödinger Equation

Du Jiang, Xuelan Wen, Yixiao Chen +8

We demonstrate, for the first time, that neural scaling laws can deliver near-exact solutions to the many-electron Schrödinger equation across a broad range of realistic molecules.…

physics.comp-ph2023★ 1 cited

Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlo

Ruichen Li, Haotian Ye, Du Jiang +8

Neural network-based variational Monte Carlo (NN-VMC) has emerged as a promising cutting-edge technique of ab initio quantum chemistry. However, the high computational cost of exis…

cs.LG2022★ 12 cited

Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective

Bohang Zhang, Du Jiang, Di He +1

Designing neural networks with bounded Lipschitz constant is a promising way to obtain certifiably robust classifiers against adversarial examples. However, the relevant progress f…

cs.LG2021★ 6 cited

Boosting the Certified Robustness of L-infinity Distance Nets

Bohang Zhang, Du Jiang, Di He +1

Recently, Zhang et al. (2021) developed a new neural network architecture based on -distance functions, which naturally possesses certified robustness by…