A novel artificial intelligence (AI) technique that uses machine learning (ML) methodologies combines several algorithms, which were developed by ThetaRay, Inc., is applied to NASA's Transiting Exoplanets Survey Satellite (TESS) dataset to identify exoplanetary candidates.
We further propose $\ell_0$-Deep CCA for solving the problem of non-linear sparse CCA by modeling the correlated representations using deep nets.
Tensor products of 1D qWPs provide a diversity of 2D qWPs oriented in multiple directions.
This paper provides a new similarity detection algorithm.
We propose to set a scale parameter that is tailored to one of two types of tasks: classification and manifold learning.
The problem of learning from seismic recordings has been studied for years.
Many of the existing methods produce a low dimensional representation that attempts to describe the intrinsic geometric structure of the original data.
We prove that the error of the proposed algorithm is bounded.
Such approach is harder to deceive and we show that only a few file fragments from a whole file are needed for a successful classification.