We are interested in the widespread problem of clustering documents and finding topics in large collections of written documents in the presence of metadata and hyperlinks.
We present the Additive Poisson Process (APP), a novel framework that can model the higher-order interaction effects of the intensity functions in stochastic processes using lower dimensional projections.
We propose the online early stopping algorithm and show that a neural network trained using this algorithm can track a function changing with unknown dynamics.
This paper proposes a new method for determining similarity and anomalies between time series, most practically effective in large collections of (likely related) time series, by measuring distances between structural breaks within such a collection.
We present a novel blind source separation (BSS) method, called information geometric blind source separation (IGBSS).