Mercoledi 18 Dicembre 2019, presso il nuovo edificio del Collegio Carlo Alberto, in Piazza Arbarello 8, Torino, si terranno i seguenti due seminari:

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ore 11.00 Stefano PELUCHETTI (Cogent Labs Tokyo)

DEEP NEURAL NETWORKS AND STOCHASTIC PROCESSES

We review deep neural networks and their training, and then focus on the connection between neural networks and stochastic processes. Deep neural networks at initialization correspond to prior models in function space, and under appropriate assumptions they converge to Gaussian processes in the limit of infinite width. While this connection holds "a priori", it is possible to derive similar results for "a posteriori" training via gradient descent. In this talk we discuss these and related results, and we introduce a recent line of research which generalizes established results to non-Gaussian initializations and stochastic processes arising in the limit of infinite depth. ————————————————————————

ore 12.00 Daniel KOWAL (Rice University)

SCALABLE BAYESIAN INFERENCE AND SUMMARIZATION FOR FUNCYIONAL DATA

Modern scientific monitoring systems, such as wearable and implantable devices, commonly record data over a continuous domain at high resolutions. These functional data are high-dimensional, strongly correlated, and usually measured concurrently with other variables of interest. Bayesians models for functional data are particularly appealing: they accommodate multiple dependence structures, handle missing or irregularly-spaced data, and provide regularization via shrinkage priors. However, these models are often complex, computationally intensive, and difficult to interpret. This talk will focus on two fundamental challenges for Bayesian functional data analysis: (1) constructing sufficiently flexible and scalable functional regression models and (2) extracting interpretable posterior summaries. The proposed modeling framework is nonparametric and uses an unknown functional basis to learn prominent functional features, which are associated with scalar predictors within a regression model. A customized projection-based Gibbs sampler provides posterior inference with linear time complexity in the number of predictors, which is empirically faster than existing frequentist and Bayesian alternatives. Using the posterior distribution, a decision theoretic approach for Bayesian variable selection is developed, which identifies a subset of covariates that retains nearly the predictive accuracy of the full model. The methodology is applied to actigraphy data to investigate the association between intraday physical activity and responses to a sleep questionnaire. ————————————————————————

Tutti gli interessati sono invitati a partecipare. Il seminario è organizzato dalla "de Castro" Statistics Initiative (www.carloalberto.org/stats http://www.carloalberto.org/stats) in collaborazione con il Collegio Carlo Alberto.

Cordiali saluti, Pierpaolo De Blasi

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