Ana Lucic has developed a framework for explaining predictions of machine learning models that could improve heart examinations for underserved communities.
Engineers from the University of Cambridge have developed a machine learning algorithm that can detect and correct a wide variety of different errors in real time.
Using Graph Neural Networks, we trained Generative Adversarial Networks to correctly predict the coherent orientations of galaxies in a state-of-the-art cosmological simulation.
We present auton-survival – a comprehensive Python code repository of user-friendly, machine learning tools for working with censored time-to-event data.
We recently built the Berkeley Crossword Solver (BCS), the first computer program to beat every human competitor in the world’s top crossword tournament.
The expressivity of current deep probabilistic models can be improved by selectively prioritizing statistical dependencies between latent variables that are potentially distant from each other.
How we depict the state of technology (imagined, current or future) visually and verbally, helps us position ourselves in relation to what is already there and what is coming.