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Making Graph Neural Networks Worth It for Low-Data Molecular Machine
  Learning

Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning

24 November 2020
Aneesh S. Pappu
Brooks Paige
    GNN
    AI4CE
ArXivPDFHTML

Papers citing "Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning"

4 / 4 papers shown
Title
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave
  Functions
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions
Nicholas Gao
Stephan Günnemann
21
36
0
11 Oct 2021
Molecular machine learning with conformer ensembles
Molecular machine learning with conformer ensembles
Simon Axelrod
Rafael Gómez-Bombarelli
AI4CE
17
49
0
15 Dec 2020
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness
  of MAML
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML
Aniruddh Raghu
M. Raghu
Samy Bengio
Oriol Vinyals
177
639
0
19 Sep 2019
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
329
11,684
0
09 Mar 2017
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