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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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Publikováno v:
Journal of Computational Biology; Nov2023, Vol. 30 Issue 11, p1226-1239, 14p
