Interacting systems are prevalent in nature, from dynamical systems in physics to complex
societal dynamics. The interplay of components can give rise to complex behavior, which can …

We propose to reinterpret a standard discriminative classifier of p(y|x) as an energy based
model for the joint distribution p(x,y). In this setting, the standard class probabilities can be …

Given the ubiquity of deep neural networks, it is important that these models do not reveal
information about sensitive data that they have been trained on. In model inversion attacks, a …

Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning
research, with a particular focus towards sequence-to-sequence models. Lingvo models …

In this study, we investigated and compared solitary and collaborative modes of game-based
learning in promoting students’ science learning and motivation. A total of fifty seventh …

Open World Object Detection (OWOD) is a new and challenging computer vision task that
bridges the gap between classic object detection (OD) benchmarks and object detection in the …

Invertible neural networks (INNs) have been used to design generative models, implement
memory-saving gradient computation, and solve inverse problems. In this work, we show that …

Pre-trained multi-modal vision-language models (VLMs) are becoming increasingly popular
due to their exceptional performance on downstream vision applications, particularly in the …

Speaker embedding models that utilize neural networks to map utterances to a space where
distances reflect similarity between speakers have driven recent progress in the speaker …

The amount of raw information available for basketball analytics has been given a great
boost with the availability of player tracking data. This facilitates detailed analyses of player …