[Seminario] Invitación Grupo de lectura aprendizaje de máquinas Jueves 22 de Septiembre a las 14:00 hrs.

[Maria Ines]

Estimados Académicos y alumnos,

Se les invita para este Jueves 22 de Septiembre a las 14:00 hrs, al 
Grupo de lectura aprendizaje de máquinas,  este se realizará en la Sala 
Multimedia del Sexto Piso, Beauchef 851, Torre Norte.

*GLAM #1: The Gaussian Process Convolution Model (Felipe Tobar)
Cuándo: 22/9/16, 1400hrs
Dónde: Sala multimedia, 6to piso, CMM
*
Tobar, Bui and Turner, "Learning Stationary Time Series using Gaussian 
Processes with Nonparametric Kernels", Neural information processing 
systems, 2015.

*Abstract: *We introduce the Gaussian Process Convolution Model (GPCM), 
a two-stage nonparametric generative procedure to model stationary 
signals as the convolution between a continuous-time white-noise process 
and a continuous-time linear filter drawn from Gaussian process. The 
GPCM is a continuous-time nonparametric-window moving average process 
and, conditionally, is itself a Gaussian process with a nonparametric 
kernel defined in a probabilistic fashion. The generative model can be 
equivalently considered in the frequency domain, where the power 
spectral density of the signal is specified using a Gaussian process. 
One of the main contributions of the paper is to develop a novel 
variational free-energy approach based on inter-domain inducing 
variables that efficiently learns the continuous-time linear filter and 
infers the driving white-noise process. In turn, this scheme provides 
closed-form probabilistic estimates of the covariance kernel and the 
noise-free signal both in denoising and prediction scenarios. 
Additionally, the variational inference procedure provides closed-form 
expressions for the approximate posterior of the spectral density given 
the observed data, leading to new Bayesian nonparametric approaches to 
spectrum estimation. The proposed GPCM is validated using synthetic and 
real-world signals.

------Próximas sesiones (tentativa):

1) The Gaussian Process Convolution Model (Felipe Tobar, 22/9)
3) Sparse and online Gaussian Processes (Christopher Ley, 29/9)
2) Warped Gaussian Processes (Gonzalo Ríos, 6/10)
4) Multi-output Gaussian processes (Gabriel Parra, 13/10)
5) Identificación de sistemas usando kernels (Alejandro Bernardín, 20/10)
6) Inferencia de Monte Carlo (Donato Vásquez, 27/10)
7) Inferencia Variacional  (F. Tobar + I. Castro, 3/11)
8) Procesos de Dirichlet (Joaquín Rojas, 17/11)
9) Deep Neural Networks (Matías Silva, 24/11)
10) Random Forests (Romain Gouron, 1/12)
11) Probabilistic Graphical models (Ignacio Reyes, 8/12)



Esperando contar con su presencia, les saluda,

Ma. Inés Rivera
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