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, ...

Abstract: Hyperspectral anomaly detection (HAD) aims to identify targets that are significantly different from their surrounding background, employing an unsupervised paradigm. Recently, detectors ...

ABSTRACT: Video-based anomaly detection in urban surveillance faces a fundamental challenge: scale-projective ambiguity. This occurs when objects of different physical sizes appear identical in camera ...

Tumors are heterogeneous diseases driven by diverse molecular abnormalities, resulting in considerable variability both across patients and within individual tumors 1,2,3. With the advent of ...

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 ...

Dimensionality reduction is a method used in machine learning and data science to reduce the dimensions in a dataset. While linear methods are generally less effective at dimensionality reduction than ...

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 ...

This paper is a valuable step in multi-subject behavioral modeling using an extension of the Variational Autoencoder (VAE) framework. Using a novel partition of the latent space and in tandem with a ...

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, ...