CS 598 LAZ: Cutting-Edge Trends in Deep Learning and Recognition
Instructor: Svetlana Lazebnik (slazebni -at- illinois.edu)
TA: Arun Mallya (amallya2 -at- illinois.edu)
Always check announcements on Piazza for short-notice changes to instructor and TA office hours!
Contents: topic list, requirements, schedule, resources
Important links: lecture videos,
Piazza (announcements, discussion board),
Compass (submission of project deliverables, grades)
OverviewThis is an advanced graduate seminar studying current research literature on trends and topics in deep learning, primarily applied to computer vision and language. Topics include state-of-the-art neural architectures and training techniques, recurrent models, neural generative models (adversarial networks and variational autoencoders), deep reinforcement learning, self-supervised learning, language and image-language models, and applications to audio and robotics. Requirements include a group presentation, a final project, literature reviews, peer grading, and participation (see details below).
Prerequisites: Equivalent of introductory courses to machine learning and computer vision, and working knowledge of standard feedforward convolutional neural networks (this course is not an introduction to deep learning).
Detailed Topic List and Readings
Group presentation (50%)Students will form groups of up to three and jointly develop a lecture on an assigned topic to be delivered on an assigned date. Each group member must deliver a portion of the presentation. See here for a detailed list of topics with suggested initial reading lists.
Guidelines for creating a successful presentation:
Group project (30%)You are encouraged to work on the project with your presentation group, but feel free to form a different group if you want (groups should not be larger than three). The project may take the following forms:
Peer grading reports (10%)Each student will be assigned to grade two presentations and will have to turn in two peer grading reports (DOC, PDF) in the course of the semester. Each peer grading report is worth 5% of your total course grade, so please take it seriously. These reports serve two purposes: to provide constructive feedback to your fellow students, and to encourage you to engage in depth in topics other than your own. Reports will be anonymous to the other students, but not to the instructor. The scores in the reports will be used to calculate the peer portion of the presentation grade for the respective team, and with rare exceptions, they will be shared with the team (but not with the class more broadly).
Reports should be submitted by email to Lana. In the subject of the email, put "mm/dd peer grading report" (where mm/dd is the date of the lecture you are reviewing). For Tuesday presentations, the reports are due by the end of Friday of the same week, and for Thursday presentations, they are due by the end of the following Sunday. Late reports will be penalized 20% (or 1% of your total course grade) for each day they are late.
Participation (10%)You are expected to come to class most days and participate in discussions both during class and on the Piazza discussion board (a thread for questions and comments will be created for each lecture).
Schedule (in progress)
Guides to deep learning
Tutorials, blogs, demos