Advanced Topics in Deep Learning
Time and Location
- Tuesday, Thursday 1:00-2:20 PM Eastern Time
- Instructor: Chen Sun (firstname.lastname@example.org)
- Office hour: 10:00 to 11:00am TThu, or by appointment
- Classroom: CIT 241
In-class participation is required unless otherwise approved by the instructor.
Zoom link can be found in the course syllabus.
Lectures will be livestreamed, and recorded for asynchronous viewing.
WWelcome to CSCI 2952N! This course aims at preparing graduate-level students the research knowledge they need to apply Deep Learning techniques for their own research. Over the past few years, there has been tremendous success in developing unified neural architectures that achieve state-of-the-art performance on language understanding (GPT-3), visual perception (ViT), and even protein structure prediction (AlphaFold). We plan to understand how they work, and how the success of such unified models can give rise to further developments on self-supervised learning, a technique that trains machine learning models without requiring labeled data; and multimodal learning, a technique that utilizes multiple input sources, such as vision, audio, and text. We will also study recent attempts to interpret these models, thus revealing potential risks on model bias. The course is organized as a combination of paper reading, student presentations, and invited guest lectures. It also requires the students to work on a final project that explores a novel direction they choose along the line of the papers we cover.
This is a seminar course aimed for PhD students and students who would like to further pursue a career that utilizes deep learning either in academia or in industry. Students are expected to feel comfortable reading 3-4 research papers per week (6 to 8 hours). They are expected to propose a final group project idea with contributions sufficient for a premier Machine Learning Conference (e.g. NeurIPS, ICLR) workshop or above. This class may be used for capstone.
- 40% Final project
- 20% Mini projects
- 20% In-class presentation
- 20% Paper reading and participation
For details please refer to the syllabus.
Academic Integrity & Collaboration Policy
Academic dishonesty will not be tolerated. This includes cheating, lying about course matters, plagiarism, or helping others commit a violation. Plagiarism includes reproducing the words of others without both the use of quotation marks and citation. Students are reminded of the obligations and expectations associated with the Brown Academic and Student Conduct Codes.
Discussion of course material with your classmates is both permitted and encouraged. However, showing, copying, or other sharing of actual code or verbatim answers to written questions is forbidden. This policy will be enforced.
Diversity & Inclusion
Our intent is that this course provides a welcoming environment for all students who satisfy the prerequisites. All members of the CS community, including faculty and staff, are expected to treat one another in a professional manner. If you feel you have not been treated in a professional manner by any of the course staff, please contact either the instructor, Ugur Cetintemel (Dept. Chair), Tom Doeppner (Vice Chair) or Laura Dobler (diversity & inclusion staff member). We will take all complaints about unprofessional behavior seriously.
Brown welcomes students from all around the country and the world, and their unique perspectives enrich our learning community. To empower students whose first language is not English, an array of support is available on campus, including language and culture workshops and individual appointments. For more information, contact the English Language Learning Specialists at email@example.com.
Brown University is committed to full inclusion of all students. Please inform the instructor if you have a disability or other condition that might require accommodations or modification of any of these course procedures. You may email the instructor, come to office hours, or speak with him after class, and your confidentiality is respected. We will do whatever we can to support accommodations recommended by SEAS. For more information contact Student and Employee Accessibility Services (SEAS) at 401-863-9588 or SEAS@brown.edu. Students in need of short-term academic advice or support can contact one of the deans in the Dean of the College office.
Being a student can be very stressful. If you feel you are under too much pressure or there are psychological issues that are keeping you from performing well at Brown, we encourage you to contact Brown’s Counseling and Psychological Services (CAPS). They provide confidential counseling and can provide notes supporting extensions on assignments for health reasons.