Multivariate Time Series,Self-supervised Learning,Time Series,Time Series Data,Variate,Anomaly Detection,Anomaly Score,Artificial Intelligence Training,Autoencoder ...

Self-supervised learning approaches, such as masked autoencoder (MAE) reconstruction and contrastive learning, offer a promising solution by reducing reliance on labeled data. Nonetheless, transformer ...

With New York adding Dart to the roster, let's take a look at the Giants' new quarterback depth chart. The Giants trading up to get a dynamic playmaker like Dart may mean Wilson could have some ...

Their framework, called Information Contrastive Learning (I-Con), shows that a variety of different algorithms including classification, regression, large language modeling, clustering, dimensionality ...

The selected significant features were used to train a novel deep-learning classifiers. We designed a graph-informed convolutional autoencoder called GICA to extract high-level features from the ...

Self-supervised learning has recently achieved great success in representation learning without human annotations. The dominant method — that is contrastive learning is generally based on instance ...

SPELL is a novel spatial-temporal graph learning framework for active speaker detection (ASD). It can model a minute-long temporal context without relying on computationally expensive networks.

Among these methods, many rely on the assumption that the most likely graph is among those with the fewest recombination events. In this paper, we propose a new approach to build maximum parsimony ...

The data displayed for this chart goes back to 1996, however we hope to be able to offer deeper historic information at a future point ...