Time-Frequency Analysis as Probabilistic Inference

TL;DR: This paper devise an approximation whose complexity grows linearly with the number of pseudo-datapoints and calibrating the approximation using a Kullback-Leibler (KL) minimization, and demonstrates the validity of this approach on a set of challenging regression tasks including missing data imputation for audio and spatial datasets.

TL;DR: This work establishes WF as a meaningful and capable resource pertinent to general distance-based applications of time series, and implements WF for time series classification using parametric/non-parametric classifiers and compared to other classical metrics.

TL;DR: A joint probabilistic model for signals, observations and spectra is proposed, where SE is addressed as an inference problem and Bayes' rule is applied to find the analytic posterior distribution of the spectrum given a set of observations.

TL;DR: A new encoding approach for natural sounds is considered, which combines a model of early auditory processing with maximal causes analysis (MCA), a sparse coding model which captures both the non-linear combination rule and non-negativity of the data.

TL;DR: In this paper, the clssical filleting and prediclion problem is re-examined using the Bode-Shannon representation of random processes and the?stat-tran-sition? method of analysis of dynamic systems.

TL;DR: An algorithm for non-negative matrix factorization is demonstrated that is able to learn parts of faces and semantic features of text and is in contrast to other methods that learn holistic, not parts-based, representations.

TL;DR: In this article, the authors consider statistical mechanics as a form of statistical inference rather than as a physical theory, and show that the usual computational rules, starting with the determination of the partition function, are an immediate consequence of the maximum-entropy principle.

TL;DR: The gaussian processes for machine learning is universally compatible with any devices to read, and is available in the digital library an online access to it is set as public so you can get it instantly.