SORA-TABA workshop will be held on Wednesday, April 30th, 2014
University of Toronto
Health Sciences Building (the auditorium – HS610),
155 College Street, Toronto ON.
Recent Advanced in Deep Learning:
Learning Structured, Robust, and Multimodal Models
Building intelligent systems that are capable of extracting meaningful representations from high-dimensional data lies at the core of solving many Artificial Intelligence tasks, including visual object recognition, information retrieval, speech perception, and language understanding.
In this talk I will first introduce a broad class of hierarchical probabilistic models called Deep Boltzmann Machines (DBMs) and show that DBMs can learn useful hierarchical representations from large volumes of high-dimensional data with applications in information retrieval, object recognition, and speech perception. I will then describe a new class of more complex models that combine Deep Boltzmann Machines with structured hierarchical Bayesian models and show how these models can learn a deep hierarchical structure for sharing knowledge across hundreds of visual categories, which allows accurate learning of novel visual concepts from few examples. Finally, I will introduce deep models that are capable of extracting a unified representation that fuses together multiple data modalities. I will show that on several tasks, including modelling images and text, video and sound, these models significantly improve upon many of the existing techniques.
Ruslan Salakhutdinov
Assistant Professor,
Department of Computer Science and
Department of Statistical Sciences
University of Toronto
SSC Student Conference in Toronto
Date: May 24, 2014
Location: University of Toronto
Link
SSC Annual Meeting in Toronto
Date: May 25 to May 28, 2014
Location: University of Toronto
Link
Title: Using Animal Instincts to Find Efficient Experimental Designs
Speaker: Weng Kee Wong, Dept of Biostatistics, UCLA
ABSTRACT
Using popular models from the biological and pharmaceutical sciences as examples, I demonstrate how particle swarm optimization (PSO) searches for different types of optimal experimental designs in dose response studies, including mini-max types of optimal designs where effective algorithms to find such designs have remained elusive until now.
Topic: Interrupted time series
Presenter: Yan Chen, St. Michael’s Hospital; TARGet Kids project
Abstract: more details to follow
When: Monday October 20th, 2014
6-7 pm seminar
7 pm dinner (optional, but registration required)
Where: Room 696, UoT Health Sciences Building,
155 College Street <----------Note different location
Cost: Seminar - free
Dinner - $15 (students $5)
To attend, please RSVP by or on Friday the 17th of October:
* for seminar only, please email us by clicking here: mailto:taba.exec@gmail.com?subject=SeminarOnly
* for seminar and dinner, please email us by clicking here: mailto:taba.exec@gmail.com?subject=SeminarAndDinner
If you plan to attend the dinner, we accept a mailed cheque, Interac email money transfer or cash at the event. If you have to cancel the dinner we request a 48 hour notice otherwise TABA has to pay for it. For Interac email money transfer (not PayPal): do the transfer via your bank to taba.exec@gmail.com and send a separate email via your own email program to us (taba.exec@gmail.com) giving the answer to the security question you used so that we can accept the transfer.
For cheques: mail cheque, payable to TABA, to
Lorinda Simms
Eli Lilly Canada Inc.
3650 Danforth Ave
Toronto, ON
M1N 2E8
Topic: Maternal and Child Health Gains in Afghanistan in the Post-Taliban Era: A Synthesis of Big Data (Presenter: Nadia Akseer)
You are invited to join our statistics seminar next week on Oct 15 at 2pm (Nross 638). The speaker is Dr. Yinglin Qin from University of Waterloo.
Here is the abstract. Hope to see you there.
Title:� Testing the order of a population spectral distribution for
high-dimensional data
�Abstract:
Yingli Qin
Large covariance matrices play a fundamental role in various high-dimensional statistics. Investigating the limiting behavior of
�the eigenvalues can reveal informative structures of large covariance
�matrices, which is particularly important in high-dimensional
�principal component analysis and covariance matrix estimation. In this
paper, we propose a framework to test the number of distinct
population eigenvalues for large covariance matrices, i.e. the order
of a Population Spectral Distribution. The limiting distribution of
our test statistic for a Population Spectral Distribution of order 2 is
developed along with its (N,p) consistency. We will also report
some simulation results and real data analysis.
Topic: Statistics in the Wild – collecting and communicating data across cultures
Presenter: Heather Krause MSc PStat, CEO Datassist