[Colloquium] talk by Sarath Chandar this Friday at 11 am

[Karl Stratos via Colloquium]

Hi all,

Sarath Chandar from the University of Montreal is visiting TTIC this
Friday. His talk is at 11 am and he will be around for the day until 5 pm.

Please let me know if you'd like to meet with the speaker before/after the
talk, and if you'd like to join for lunch.



When:     Friday, June 30th at 11:00 am

Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526

Who:      Sarath Chandar, University of Montreal

*Title:* Memory Augmented Neural Networks


*Abstract:*

Designing of general-purpose learning algorithms is a long-standing goal of
artificial intelligence. A general purpose AI agent should be able to have
a memory that it can store and retrieve information from. Despite the
success of deep learning in particular with the introduction of LSTMs and
GRUs to this area, there are still a set of complex tasks that can be
challenging for conventional neural networks. Those tasks often require a
neural network to be equipped with an explicit, external memory in which a
larger, potentially unbounded, set of facts need to be stored. They include
but are not limited to, reasoning, planning, episodic question-answering
and learning compact algorithms. Recently two promising approaches based on
neural networks to this type of tasks have been proposed: Memory Networks
and Neural Turing Machines.



In this talk, I will give an overview of this new paradigm of "neural
networks with memory". I will present a unified architecture for Memory
Augmented Neural Networks (MANN) and discuss the ways in which one can
address the external memory and hence read/write from it. In the second
half of the talk, we will focus on recent advances in MANN which focus on
the following questions: How can we read/write from an extremely large
memory in a scalable way? How can we design efficient non-linear addressing
schemes using hard attention? How can we model long term dependencies in a
problem using MANNs? The answer to any one of these questions introduces a
variant of MANN. I will conclude the talk with several open challenges in
MANN.



*Speaker Bio:* Sarath Chandar is currently a PhD student in University of
Montreal under the supervision of Yoshua Bengio and Hugo Larochelle. His
work mainly focuses on Deep Learning for complex NLP tasks like question
answering and dialog systems. He also investigates scalable training
procedure and memory access mechanisms for memory network architectures. In
the past, he has worked on multilingual representation learning and
transfer learning across multiple languages. His research interests include
Machine Learning, Natural Language Processing, Deep Learning, and
Reinforcement Learning. Before joining University of Montreal, he was a
Research Scholar in IBM Research India for a year. He has completed his MS
by Research in IIT Madras. To view the complete publication list and
speaker profile, please visit: http://sarathchandar.in/

Host: Karl Stratos
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.cs.uchicago.edu/pipermail/colloquium/attachments/20170627/62fc9049/attachment-0001.html>