Course Project
The course project will give the students a chance to explore deep generative modeling in greater detail. Course projects will be done in groups of up to 3 students and can fall into one or more of the following categories:
- Application of deep generative models on a novel task/dataset.
- Algorithmic improvements to the evaluation, learning and/or inference of deep generative models.
- Theoretical analysis of any aspect of existing deep generative models.
Proposal
Your proposal should give the title of the project, the project category, the names of your team members, their NetID, and a 300-500 word description of what you plan to do. It should contain the following information.
- Motivation: What problem are you tackling? Is this an application or a theoretical result?
- Method: What machine learning techniques are you planning to apply or improve upon and how?
- Future work: What experiments are you planning to perform or what theorems do you want to prove?
Please submit the proposal via Gradescope.
Milestone
The milestone submission should describe what you've accomplished so far, and briefly say what else you plan to do. The format should be the same as of the final project, with a maximum length of 3 pages (excluding references). The goal is to make sure that you are on track to finish the final project.
- Motivation: What problem are you tackling? Is this an application or a theoretical result?
- Method: What machine learning techniques are you planning to apply or improve upon and how?
- Preliminary experiments: Describe the experiments that you've run, the outcomes, and any error analysis that you've done. You should have tried at least one baseline.
- Future work: What else do you plan to do?
Please submit the milestone via Gradescope and make sure to submit as a team.
Final Writeup
The final writeup should describe all the work you did for your course project and present the main results. You can think of it as a technical report that presents your findings to a general machine learning audience.
The style and format of the writeup should be similar to that of a machine learning conference paper. The expected length is 5-8 pages, excluding references. There are no strict requirements on the structure of the final writeup, but one way of structuring it would be include the following sections, which are fairly standard for a research paper.
- Abstract: Summarize the problem, novel contributions, and results in one paragraph.
- Introduction: Provide motivation for the problem and expand upon the overview in the abstract.
- Background: Briefly summarize the background knowledge needed to understand the work.
- Method: Describe the methods that will be used or implemented in the paper.
- Theoretical analysis: If you are doing a theory project, describe your theoretical results here.
- Experimental analysis: Describe in detail your experiments.
- Discussion and Prior Work: Discuss the key takeaways from your experiments. Put your results in the context of previous work
- Conclusion. You may summarize the paper or talk about open problems and open directions.
Regardless of how the writeup is structured, please make sure to cover the following points.
- Motivation: What problem are you tackling? Why is it interesting? What type of project will this be (application, method, theory)?
- Method: What machine learning techniques are you planning to apply or improve upon and how? Make sure to describe them in detail and provide enough context for the reader to understand the methods at least at a high level. Provide any background that is necessary for that.
- Experiments: Describe the experiments that you've run, the outcomes, and any error analysis that you've done. Make sure that the setup is described in enough detail for someone else to reproduce your results. Also, if you have an experimental project, make sure to provide a detailed experimental analysis. Things you should consider including are: train/test performance, learning curves, model samples, error analyses, ablation analyses, etc. Most projects should also include baselines.
- Theory: If doing a theory project, state your results formally as theorems. Make sure that all the symbols are defined. Also, the best presentation of theoretical results tends to also explain the results in plain language and conveys the intuition behind them.
- Context: Explain how you build upon previous work and how your results compare to what has been done previously.
Writeups will be evaluated for their presentation clarity, their relevance to topics taught in the course, the novelty of contributions, and the technical quality and level of depth in the experimental or theoretical analyses.
The PDFs of the projects will be shared on the course website. If you do not want your project to be posted, please let the instructor know before the submission deadline.
Please submit the writeup via Gradescope and make sure to submit as a team.
Presentation
Students will be asked to deliver one hour-long presentation in the second half of the course. Presentations can be done individually or in groups of two and should cover 1-2 research papers out of a list posted on the course website (“Additional readings” on the syllabus page).
- The expected length of each presentation is 60-70 minutes. Presentations will be followed by 20-30 minutes of discussion. Students are asked to conclude their presentation with an initial set of discussion topics for the team.
- There will be ten presentation slots in the second half of the class from 03/17 to 04/23. Students should email the instructor by 03/01 to reserve a presentation slot and choose a presentation topic. Slots will be filled on a first come first served basis.
- Each presentation team needs to (1) send a presentation outline to the instructor at least two weeks before the talk and (2) send presentation slides at least two days before the talk.
Format
The ideal presentation will touch the following topics:
- Motivation for the problem being studied; why is it interesting?
- context and previous work in this area;
- high-level summary of the novel ideas and contributions in the presented papers;
- detailed explanation of the technical material in the papers;
- summary of experimental or theoretical results;
- discussion of the results;
- conclusion and open-ended questions.
Students should conclude the presentation with follow-up topics / questions to the audience to seed a discussion / q&a covering the presentation and future research directions based on these papers. They should drive the discussion and the instructor will help with that as well.