An algorithm scanned 78 million genetic variants and found patterns that shouldn't exist. Most human DNA tells a familiar story—but some of it defies explanation. Researchers discovered genetic ...

Abstract: Deep convolutional neural networks have become a dominant solution for numerous image classification tasks. However, a main criticism is the poor explainability due to the black-box ...

How do our genes determine our appearance and our susceptibility to disease? This question is central to biomedical research, and today we can sequence thousands of human genomes to identify these ...

Real estate agents will tell you that a home’s most important feature is “location, location, location.” It’s similar in neuroscience: “Location is everything in the brain,” said Bosiljka Tasic, a ...

A comprehensive collection of counterfactual explanation algorithms for time series classification with PyTorch implementations. This library provides state-of-the-art methods for generating and ...

We use heuristics to solve computationally difficult problems where optimal solutions are too expensive to deploy, hard to manage, or otherwise inefficient. Our prior work, MetaOpt, shows many of the ...

My name is Philippe. The goal of this project is to model the Elliott Wave Theory to forecast the financial markets. Once we have the model and know the parameters, we optimize it using a machine ...

1 Department of Neuroscience, ALS Center, ‘Rita Levi Montalcini’, University of Torino, Torino, and Azienda Ospedaliera Città della Salute e della Scienza, Torino, Italy 2 Department of Neuroscience, ...

Abstract: Explainable Artificial Intelligence (XAI) is a cutting-edge AI development motivated by the need for transparency of black-box models in AI systems. This transparency enhances user trust, ...