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Recent research has shown that it is possible to find interpretable directions in the latent spaces of pre-trained GANs. These directions enable controllable generation and support a variety of semantic editing operations. While previous work has foc
Externí odkaz:
http://arxiv.org/abs/2202.11772
Recently, the discovery of interpretable directions in the latent spaces of pre-trained GANs has become a popular topic. While existing works mostly consider directions for semantic image manipulations, we focus on an abstract property: creativity. C
Externí odkaz:
http://arxiv.org/abs/2112.06978
Computational models that accurately predict the binding affinity of an input protein-chemical pair can accelerate drug discovery studies. These models are trained on available protein-chemical interaction datasets, which may contain dataset biases t
Externí odkaz:
http://arxiv.org/abs/2107.05556
Akademický článek
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Autor:
Haixu Tang
This book constitutes the refereed proceedings of the 27th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2023, held in Istanbul, Turkey, during April 16–19, 2023.The 11 regular and 33 short papers presented