Jiefu Zhang

activity

20192026

most citedDeep Density: circumventing the Kohn-Sham equations via symmetry preserving neural networks

13 citations · 13 across the 2 of their papers we have counts for

collaborators

3 papers

cs.LG2026

Don't Freeze, Don't Crash: Extending the Safe Operating Range of Neural Navigation in Dense Crowds

Jiefu Zhang, Yang Xu, Vaneet Aggarwal

Navigating safely through dense crowds requires collision avoidance that generalizes beyond the densities seen during training. Learning-based crowd navigation can break under out-…

cs.LG2020

Learning the mapping $x \mapsto \sum_{i = 1}^{d} x_{i}^{2}$ : the cost of finding the needle in a haystack

Jiefu Zhang, Leonardo Zepeda-Núñez, Yuan Yao +1

The task of using machine learning to approximate the mapping $x \mapsto \sum_{i = 1}^{d} x_{i}^{2}$ with $x_{i} \in [- 1, 1]$ seems to be a trivial one. Given the knowledge of the separa…

physics.comp-ph2019★ 13 cited

Deep Density: circumventing the Kohn-Sham equations via symmetry preserving neural networks

Leonardo Zepeda-Núñez, Yixiao Chen, Jiefu Zhang +3

The recently developed Deep Potential [Phys. Rev. Lett. 120, 143001, 2018] is a powerful method to represent general inter-atomic potentials using deep neural networks. The success…

Don't Freeze, Don't Crash: Extending the Safe Operating Range of Neural Navigation in Dense Crowds

Learning the mapping x↦∑i=1d​xi2​: the cost of finding the needle in a haystack

Deep Density: circumventing the Kohn-Sham equations via symmetry preserving neural networks

Learning the mapping $x \mapsto \sum_{i = 1}^{d} x_{i}^{2}$ : the cost of finding the needle in a haystack