Personally identifiable information has been found in DataComp CommonPool, one of the largest open-source data sets used to ...

Journalists have uncovered a handful of preprint academic studies with hidden prompts instructing A.I. reviewers to give ...

We appreciate the opportunity to address the comments by Yangfan Cheng and colleagues, and Sebastian Walsh and colleagues regarding our Article on long-term gantenerumab treatment in dominantly ...

We read with great interest the Article by Randall J Bateman and colleagues investigating long-term gantenerumab treatment in dominantly inherited Alzheimer's disease.1 This important work provides ...

This paper proposes Synonymous Variational Inference, a novel variational inference that first theoretically explains the core reason for the divergence term’s existence in the perceptual image ...

Article citations More>> Tschannen, M., Bachem, O. and Lucic, M. (2018) Recent Advances in Autoencoder-based Representation Learning. arXiv: 1812.05069. has been cited by the following article: TITLE: ...

A transfer-learned hierarchical variational autoencoder model for computational design of anticancer peptides.. If you have the appropriate software installed, you can download article citation data ...

Jin, W., Barzilay, R. and Jaakkola, T. (2018) Junction Tree Variational Autoencoder for Molecular Graph Generation. International Conference on Machine Learning ...

This study aims to explore an autoencoder-based method for generating brain MRI images of patients with Autism Spectrum Disorder (ASD) and non-ASD individuals, and to discriminate ASD based on the ...

Variational autoencoders (VAEs) are a powerful class of generative models that can learn to produce realistic and diverse samples of data, such as images, text, or audio.