1. No class on May 7
This is a graduate-level
advanced course on deep learning.
We will discuss/present newest and important publications on
deep learning, specifically in natural language processing (main focus)
and computer vision.
Each student is expected to read papers before
lecture, write paper reviews, and complete
a course project (e.g., implement an existing algorithm or solve a new problem
creatively using deep learning. One team could have two students). Projects that simply apply CNN and LSTM
are not encouraged.
Neural network building experience or CS292F/CS291A: Deep Learning
for NLP or successfully finished an online deep
Enrollment Code: 08813 Instructor:
Prof. Xifeng Yan , Email:
xyan at cs.ucsb.edu
Time: Monday/Wednesday 1:00- 2:45pm,
Location: PHELP 3526 Office Hour:
Monday 3:00-4:00pm (HFH 1111)
Make-Up Lecture Time: Friday 1:00- 2:45pm,
Location: PHELP 3526 (Due
to meeting/travelling, I will frequently use this slot for make-up lectures, you
are required to attend.
Research Project Coordinator: Xiaoyong Jin (jxy9426 at gmail dot com);
PoQaa Website Coordinator: Keqian Li (keqianlicg
at gmail dot com)
Grading: Your grade will be derived from
paper reading (30%), paper review (10%), active participation
(10%), project presentation
project quality (20%), project report (15%) + Extra Credit 5% per paper
presentation volunteered by you.
Paper Reading: You are required to read every paper
carefully before lecture. We will randomly select six lectures and give a
30-minute quiz in each lecture. Each quiz has a few questions to check if
you have read the paper or not. Please bring your notebook every
lecture. If you forget, you are allowed to handwrite your answer.
Paper Review: Find two deep learning related papers
that are not presented in lecture and write a detailed 2-page review for each of
them. Your review shall include your understanding of the work, discuss
its strength and weakness, and mostly important, propose new ideas to improve it.
The first review is due on April 30; the second one is due on May 23.
A review will be graded by the quality of the paper (20%) and the quality of
your review (80%).
Twitter Following: We created a few twitter accounts (poqaa_ai,
poqaa_cs) to track the
frequently mentioned papers in the Twitter space (arxiv.org paper only).
You are welcome to follow and find valuable papers to review. You can also
check the Poqaa Trending (same
to the above accounts),
and Reddit.com machine
learning forum to find good papers. You are also welcome to join us to
improve the automatic discovery of important papers mentioned in social media.
Text Books (not required, but you'd better read it)
Deep Learning, An MIT Press book, Ian Goodfellow and Yoshua Bengio and Aaron
Lecture notes (will
be available later)