––WiMi Hologram Cloud Inc., a leading global Hologram Augmented Reality Technology provider, today announced that it developed an innovative technology, attentional autoencoder network for ...

Compared to using PCA for dimensionality reduction, using a neural autoencoder has the big advantage that it works with source data that contains both numeric and categorical data, while PCA works ...

The team proposed a novel representation learning method based on serial autoencoders for personalized recommendation.

Dr. James McCaffrey from Microsoft Research presents a complete program that uses the Python language LightGBM system to create a custom autoencoder for data anomaly detection. You can easily adapt ...

Ziwei Zhu, Assistant Professor, Computer Science, College of Engineering and Computing (CEC), received funding for the project: “III: Small: Harnessing Interpretable Neuro-Symbolic Learning for ...

A transfer-learned hierarchical variational autoencoder model for computational design of anticancer peptides. Authors: Farzad Midjani, Hossein Abbasi, Mahdi Malekpour, Shahin Yaghoobi, Sina Abdous, ...

Understanding what is happening inside the “black box” of large protein models could help researchers choose better models ...

With help from artificial intelligence, MIT researchers have designed novel antibiotics that can combat two hard-to-treat infections: drug-resistant Neisseria gonorrhoeae and multi-drug-resistant ...

We propose an unsupervised method for detecting adversarial attacks in inner layers of autoencoder (AE) networks by maximizing a non-parametric measure of anomalous node activations.

MIT researchers used AI to develop novel antibiotics NG1 and DN1, effective against drug-resistant gonorrhoea and MRSA, ...