CS291A - Special Topics in Deep Learning home | schedule



Abstract: This is a graduate-level research course on deep learning.  We will discuss/present newest and important publications in deep learning, specifically Transformer-based techniques in the areas of knowledge base, question answering, conversational AI, natural language processing and multimodal learning.

Each student is expected to read papers before lecture, write paper reviews, present papers,  and complete a research-quality 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, LSTM or Transformers are not encouraged.

Prerequisites: Neural network building experience or successfully finished an introductory deep learning course.

Instructor: Prof. Xifeng Yan

Time: Monday/Wednesday 11:00- 12:50pm, Location: PHELP 3526   Office Hour:  Monday 1:30-2:30pm, Henley Hall 2017

TA: N/A. 

Grading: Your grade will be derived from paper review (15%),  paper presentation (15%), midterm quiz (15%), project presentation (15%), and project (40%)

Paper Reading: You are required to read every paper carefully before lecture.

Paper Review: Find two deep learning papers that are not presented in lecture but highly related to your project, and write a detailed 2-page review.  Your review shall include your understanding of the work, discuss its strength and weakness, and mostly important, propose new ideas to improve it.    A review will be graded by the quality of the paper (20%) and the quality of your review (80%).

Text Books (not required)