Title: Deep Learning, Where are you going?
Date: April 18 (Tuesday)
Time: 4:00pm
Place: Troy 2018

There are three axes along which advances in machine learning and deep 
learning happen. They are (1) network architectures, (2) learning algo-
rithms and (3) spatio-temporal abstraction. In this talk, I will des-
cribe a set of research topics I've pursued in each of these axes. For 
network architectures, I will describe how recurrent neural networks, 
which were largely forgotten during 90s and early 2000s, have evolved 
over time and have finally become a de facto standard in machine trans-
lation. I continue on to discussing various learning paradigms, how 
they related to each other, and how they are combined in order to build 
a strong learning system. Along this line, I briefly discuss my latest 
research on designing a query-efficient imitation learning algorithm 
for autonomous driving. Lastly, I present my view on what it means to 
be a higher-level learning system. Under this view each and every end-
to-end trainable neural network serves as a module, regardless of how 
they were trained, and interacts with each other in order to solve a 
higher-level task. I will describe my latest research on trainable de-
coding algorithm as a first step toward building such a framework.

Kyunghyun Cho is an assistant professor of computer science and data 
science at New York University. He was a postdoctoral fellow at Uni-
versity of Montreal until summer 2015, and received PhD and MSc degrees 
from Aalto University early 2014. He tries best to find a balance among 
machine learning, natural language processing and life, but often fails 
to do so.

Host:  Prof. Heng Ji (x2103)