For the first time, a research team has demonstrated an artificial intelligence semiconductor technology that integrates the ...

When AI models like Claude process words internally, they treat them as 'activation values,' which are long sequences of numbers that encode thoughts and are difficult to decipher. For many years, ...

Abstract: The backdoor attack poses a new security threat to deep neural networks (DNNs). The existing backdoor often relies on visible universal triggers to make the backdoored model malfunction, ...

Reference implementation for a variational autoencoder in TensorFlow and PyTorch. I recommend the PyTorch version. It includes an example of a more expressive variational family, the inverse ...

Abstract: Image reconstruction-based methods with autoencoder have been widely used for unsupervised anomaly detection. By training the reconstruction on normal ...

Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that are different in some way from the majority of the source items. Data anomaly ...

Same as traditional autoencoders, VAE architecture has two parts: an encoder and a decoder. Traditional AE models map inputs into a latent-space vector and reconstruct the output from this vector. VAE ...

This repository provides code to accompany the paper: Greener JG, Moffat L and Jones DT, Design of metalloproteins and novel protein folds using variational autoencoders, Scientific Reports 8:16189, ...