Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Podda, Marco"'
This work focuses on the task of property targeting: that is, generating molecules conditioned on target chemical properties to expedite candidate screening for novel drug and materials development. DiGress is a recent diffusion model for molecular g
Externí odkaz:
http://arxiv.org/abs/2312.17397
Autor:
Podda, Marco, Bacciu, Davide
The problem of labeled graph generation is gaining attention in the Deep Learning community. The task is challenging due to the sparse and discrete nature of graph spaces. Several approaches have been proposed in the literature, most of which require
Externí odkaz:
http://arxiv.org/abs/2107.08396
Autor:
Podda, Marco, Bonechi, Simone, Palladino, Andrea, Scaramuzzino, Mattia, Brozzi, Alessandro, Roma, Guglielmo, Muzzi, Alessandro, Priami, Corrado, Sîrbu, Alina, Bodini, Margherita
Publikováno v:
In iScience 15 March 2024 27(3)
Publikováno v:
PMLR 108:2240-2250 (2020)
Molecule generation is a challenging open problem in cheminformatics. Currently, deep generative approaches addressing the challenge belong to two broad categories, differing in how molecules are represented. One approach encodes molecular graphs as
Externí odkaz:
http://arxiv.org/abs/2002.12826
Graph generation with Machine Learning is an open problem with applications in various research fields. In this work, we propose to cast the generative process of a graph into a sequential one, relying on a node ordering procedure. We use this sequen
Externí odkaz:
http://arxiv.org/abs/2002.00102
The adaptive processing of graph data is a long-standing research topic which has been lately consolidated as a theme of major interest in the deep learning community. The snap increase in the amount and breadth of related research has come at the pr
Externí odkaz:
http://arxiv.org/abs/1912.12693
Experimental reproducibility and replicability are critical topics in machine learning. Authors have often raised concerns about their lack in scientific publications to improve the quality of the field. Recently, the graph representation learning fi
Externí odkaz:
http://arxiv.org/abs/1912.09893
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
In Neural Networks September 2020 129:203-221
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.