ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2310.01704
  4. Cited By
Transformers are efficient hierarchical chemical graph learners

Transformers are efficient hierarchical chemical graph learners

2 October 2023
Zihan Pengmei
Zimu Li
Chih-chan Tien
Risi Kondor
Aaron R Dinner
    GNN
ArXivPDFHTML

Papers citing "Transformers are efficient hierarchical chemical graph learners"

4 / 4 papers shown
Title
Probing Graph Representations
Probing Graph Representations
Mohammad Sadegh Akhondzadeh
Vijay Lingam
Aleksandar Bojchevski
26
10
0
07 Mar 2023
GRPE: Relative Positional Encoding for Graph Transformer
GRPE: Relative Positional Encoding for Graph Transformer
Wonpyo Park
Woonggi Chang
Donggeon Lee
Juntae Kim
Seung-won Hwang
39
74
0
30 Jan 2022
Junction Tree Variational Autoencoder for Molecular Graph Generation
Junction Tree Variational Autoencoder for Molecular Graph Generation
Wengong Jin
Regina Barzilay
Tommi Jaakkola
208
1,329
0
12 Feb 2018
MoleculeNet: A Benchmark for Molecular Machine Learning
MoleculeNet: A Benchmark for Molecular Machine Learning
Zhenqin Wu
Bharath Ramsundar
Evan N. Feinberg
Joseph Gomes
C. Geniesse
Aneesh S. Pappu
K. Leswing
Vijay S. Pande
OOD
154
1,748
0
02 Mar 2017
1