Abstract: This paper presents an autoencoder-based method for a targeted attack on deep neural network models, named AE4DNN. The proposed method aims to improve the existing targeted attacks in terms ...

This repository contains the repeatability package for the codebase of the conference paper "Scalable and Interpretable Verification of Image-based Neural Network Controllers for Autonomous Vehicles", ...

Human inventions, namely engineered systems, have relied on fundamental discoveries in physics and mathematics, e.g., Maxwell’s equations, Quantum mechanics, Information theory, etc., thereby applying ...

Hyperspectral unmixing methods are essential to exploit the capabilities of Raman spectroscopy for nondestructive, unbiased chemical characterization in a wide array of domains, from biology, ...

Spatially resolved transcriptomics (SRT) technologies, such as spatial transcriptomics (ST) (Ståhl et al., 2016), 10x Visium, and Slide-seqV2 (Stickels et al., 2021), can measure the transcript ...

Abstract: Deep convolutional neural networks (DCNNs) have achieved surpassing success in the field of computer vision, and a number of elaborately designed networks refresh the performance records in ...

Immune-related processes are important in underpinning the properties of clinical traits such as prognosis and drug response in cancer. The possibility to extract knowledge learned by artificial ...

The interplay between data symmetries and network architecture is key for efficient learning in neural networks. Convolutional neural networks perform well in image recognition by exploiting the ...

Generating synthetic data is useful when you have imbalanced training data for a particular class, for example, generating synthetic females in a dataset of employees that has many males but few ...

Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a dataset that are different from the majority for tasks like ...