Cornell University

6 Posts

High-level overview of the STEGO architecture at train and prediction steps
Cornell University

Segmented Images, No Labeled Data: Improved unsupervised learning for semantic segmentation

Training a model to separate the objects in a picture typically requires labeled images for best results. Recent work upped the ante for training without labels.
Results of survey about how AI Engineers vs US public feel about ethical issues
Cornell University

AI Engineers Weigh In on AI Ethics: Survey Shows How AI Engineers Feel About Ethical Issues

Machine learning researchers tend to trust international organizations, distrust military forces, and disagree on how much disclosure is necessary when describing new models, a new study found.
A new framework that helps models “unlearn” information selectively and incrementally
Cornell University

Deep Unlearning: AI Researchers Teach Models to Unlearn Data

Privacy advocates want deep learning systems to forget what they’ve learned. What’s new: Researchers are seeking ways to remove the influence of particular training examples, such as an individual’s personal information, from a trained model without affecting its performance, Wired reported.
Neural Body, a procedure that generates novel views of a single human character, working
Cornell University

Seeing People From a New Angle

The University of Hong Kong, and Cornell University to create Neural Body, a procedure that generates novel views of a single human character based on shots from only a few angles.
Volcano erupting
Cornell University

Predicting the Next Eruption

AI is providing an early warning system for volcanoes on the verge of blowing their top. Researchers at the University of Leeds developed a neural net that scans satellite data for indications that the ground near a volcano is swelling—a sign it may be close to erupting.
Tensorflow and Pytorch logos
Cornell University

Clash of the Frameworks

Most deep learning applications run on TensorFlow or PyTorch. A new analysis found that they have very different audiences. A researcher at Cornell University compared references to TensorFlow and PyTorch in public sources over the past year.

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