11:33 a.m.
*Speaker:
Rafael Cabañas
*Title:
InferPy: Deep Probabilistic Modeling made easy
*Abstract:
InferPy is an open-source library for deep probabilistic modeling written in Python and
running on top of Edward 2 and Tensorflow. Other existing probabilistic programming
languages possess the drawback that they are difficult to use, especially when defining
deep neural networks and probability distributions over multidimensional tensors. This
means that their final goal of broadening the number of people able to code a machine
learning application may not be fulfilled. InferPy tries to address these issues by
defining a user-friendly API which trades-off model complexity with ease of use. In
particular, this library allows users to: prototype hierarchical probabilistic models with
a simple and user-friendly API inspired by Keras; define probabilistic models with complex
constructs containing deep neural networks; create computationally efficient batched
models without having to deal with complex tensor operations; and run seamlessly on CPUs
and GPUs by relying on Tensorflow.
*Short bio:
Rafael Cabañas is a new postdoc researcher at IDSIA. In general, his research is framed
in the fields of data analysis and machine learning, and more specifically related to
uncertainty treatment with probabilistic graphical models (PGMs).
He did his Ph.D. in the Department of Computer Science and AI of the University of Granada
(Spain). During this time, he worked in the study of new algorithms and data structures
for the inference of influence diagrams, a kind of PGM for decision making. Then, he was
involved in the European project AMIDST as a researcher hired by the Aalborg University
(Denmark), where he worked in the development of a Java open-source library for the
analysis of streaming data with PGMs. This software is being used by two private sector
companies, namely the Spanish regional bank BCC and Danish IT company Hugin Expert.
In the past few months, as a postdoc researcher at the University of Almería (Spain), he
has been developing a Python library of a probabilistic programming language over GPUs,
namely InferPy. Its main purpose is to provide an easy-to-use syntaxis for the definition
of probabilistic programs with neural networks.
*Date:
Tuesday, 28th of May 2019, 12:00-13:00
*Location: Manno, Galleria 1, 2nd floor, room G1-201
*Doodle registration:
Pizza and drinks will be offered at the end of the talk. If you plan to attend, please
register in a timely fashion at the
following link so that we will have no shortage of food:
https://doodle.com/poll/84i8swhg5bv5m3ci