Using an advanced Monte Carlo method, Caltech researchers found a way to tame the infinite complexity of Feynman diagrams and ...

The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while ...

Metric learning is an important problem in machine learning. It aims to group similar observations together. Existing state-of-the-art metric learning approaches require class labels to induce a ...

The new research suggests that when animals learn a task, the brain might simultaneously use both algorithms—an unsupervised component to extract features and a supervised component to assign meaning ...