We're surrounded by machines that talk to us, and we're talking back more than ever. Synthetic voices have moved beyond novelty into everyday tools: podcast narration, virtual coaching apps, and car ...

A collaborative research team led by Professor Pan Feng from the School of New Materials at Peking University Shenzhen ...

This review explores how generative AI, through models like VAEs, GANs, transformers, and diffusion networks is ...

Article citations More>> Wang, R., Zhu, H. and Li, S. (2021) Fashion Item Generation and Manipulation via Variational Autoencoders. Proceedings of the IEEE/CVF International Conference on Computer ...

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 ...

However, I think its useful application lies in scenarios where it trains VAEs (Variational Autoencoders) to convert images or audio into latent spaces. As is well-known, GANs (Generative Adversarial ...

Yuan, Z. and Dong, D.M. (2022) Near-Infrared Spectroscopy Measurement of Contrastive Variational Autoencoder and Its Application in the Detection of Liquid Sample. Spectroscopy and Spectral Analysis, ...

Compute efficient generative AI foundation model for small molecule drug discovery start-up announces funding from leading AI and biotech investors.

Approval of new airspace procedures and/or technologies requires the evaluation of the probability of air-to-air collisions against a Target Level of Safety (e.g. 10E-9) using Collision Risk Models ...

Imaging mass spectrometry (IMS) is a technique for simultaneously acquiring the expression and distribution of molecules on the surface of a sample, and it plays a crucial role in spatial omics ...

Variational Autoencoders (VAE) on MNIST By stuyai, taught and made by Otzar Jaffe This project demonstrates the implementation of a Variational Autoencoder (VAE) using TensorFlow and Keras on the ...