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Xiang Fu

13 papers hereh-index 111.8k citations15 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author6
  • middle author5

Across the 11 of 13 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • physics.chem-ph2
  • cond-mat.dis-nn1
  • cond-mat.mtrl-sci1
  • cs.CL1
  • cs.RO1
same name
  • Xiang Fu — 13 papers, h 11
  • Xiang Fu — 7 papers, h 4
  • Xiang Fu — 6 papers, h 6
  • Xiang Fu — 5 papers, h 3
  • Xiang Fu — 5 papers, h 2
  • Xiang Fu — 4 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192026
most citedForces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

170 citations · 395 across the 13 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.RO2021

Learning to Jump from Pixels

Gabriel B. Margolis, Tao Chen, Kartik Paigwar +4

Today's robotic quadruped systems can robustly walk over a diverse range of rough but continuous terrains, where the terrain elevation varies gradually. Locomotion on discontinuous…

q-bio.BM2021

Fragment-based Sequential Translation for Molecular Optimization

Benson Chen, Xiang Fu, Regina Barzilay +1

Searching for novel molecular compounds with desired properties is an important problem in drug discovery. Many existing frameworks generate molecules one atom at a time. We instea…

cs.LG2021★ 105 cited

Crystal Diffusion Variational Autoencoder for Periodic Material Generation

Tian Xie, Xiang Fu, Octavian-Eugen Ganea +2

Generating the periodic structure of stable materials is a long-standing challenge for the material design community. This task is difficult because stable materials only exist in…

cs.LG2021★ 1 cited

Learning Task Informed Abstractions

Xiang Fu, Ge Yang, Pulkit Agrawal +1

Current model-based reinforcement learning methods struggle when operating from complex visual scenes due to their inability to prioritize task-relevant features. To mitigate this…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.