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 ...

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 ...

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 ...

Density estimation is among the most fundamental problems in statistics. It is notoriously difficult to estimate the density of high-dimensional data due to the “curse of dimensionality.” Here, we ...

tl;dr Our approach distills the residual information of one model with respect to another's and thereby enables translation between fixed off-the-shelf expert models such as BERT and BigGAN without ...