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Title:

Accelerating Molecular Graph Neural Networks via Knowledge Distillation

Document type:
Konferenzbeitrag
Contribution type:
Textbeitrag / Aufsatz
Author(s):
Ekström Kelvinius, Filip; Georgiev, Dimitar; Toshev, Artur; Gasteiger, Johannes
Abstract:
Recent advances in graph neural networks (GNNs) have enabled more comprehensive modeling of molecules and molecular systems, thereby enhancing the precision of molecular property prediction and molecular simulations. Nonetheless, as the field has been progressing to bigger and more complex architectures, state-of-the-art GNNs have become largely prohibitive for many large-scale applications. In this paper, we explore the utility of knowledge distillation (KD) for accelerating molecular GNNs. To...     »
Dewey Decimal Classification:
620 Ingenieurwissenschaften
Editor:
Oh, A.; Neumann, T.; Globerson, A.; Saenko, K.; Hardt, M.; Levine, S.
Book / Congress title:
Advances in Neural Information Processing Systems
Volume:
36
Publisher:
Curran Associates, Inc.
Year:
2023
Pages:
25761--25792
Language:
en
WWW:
https://proceedings.neurips.cc/paper_files/paper/2023/file/51ec452ca04d8ec7160e5bbaf76153f6-Paper-Conference.pdf
TUM Institution:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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