Course Project

An integral part of the course is the class project (32% of the grade), which gives students a chance to apply deep architectures discussed in class to a research oriented project. The students should work in pairs. The course project may involve

• Design of a novel end-to-end approach and its experimental analysis, or
• An extension to a recent study of non-trivial complexity and its experimental analysis.

In preparing your progress and final project reports, you should use the provided LaTeX template and submit them electronically in PDF format. Late submissions will be penalized.

Deliverables

• Proposals: Nov 9, 2022
• Project progress presentations: Dec 7, 2022
• Project progress reports: Dec 14, 2022
• Final project presentations: Jan 4, 2023
• Final reports: Jan 20, 2023

Grading

• Proposal (2%)
• Progress presentation (4%)
• Progress report (6%)
• Project presentation (8%)
• Final report and code (12%)

Project Proposal

Due: November 9, 2022 (11:59pm)

Each group should submit a project proposal (~1-2 page long) on their specific project idea by November 9, 2022. The proposal should be prepared using this LaTeX template and should provide the following:

• The research topic to be investigated,
• A list of key readings.
• Design overview,
• What data and metrics you will use,
• An approximate timeline of activities.

Progress Presentations

Due: December 7, 2022

Each project group will have ~6-8 mins to present their progresses on their projects in class. The suggested outline for the presentations are as follows:

• Problem statement and motivation (clear definition of the problem, why it is interesting and important)
• Related work
• Key technical ideas (overview of the proposed deep model)
• Experimental evaluation (datasets, evaluation metrics, applications)
• Preliminary results (discussion of the results obtained so far)

Progress Report

Due: December 14, 2022 (11:59pm)

Each group should submit a project progress report by December 14, 2022. The report should be 4-6 pages and should be prepared using this LaTeX template. In your report, please describe the following points as clearly as possible:

• Problem to be addressed. Give a short description of the problem that you will explore. Explain why you find it interesting.
• Related work. Briefly review the major works related to your research topic.
• Methodology to be employed. Describe the neural architecture that is expected to form the basis of the project. State whether you will extend an existing method or you are going to devise your own approach.
• Experimental evaluation. Briefly explain how you will evaluate your results. State which dataset(s) you will employ in your evaluation. Provide your preliminary results (if any).
• Baseline Results. Implement a simple baseline method and report its performance.
• References. This section gives a list of all related work you reviewed or used.

Final Presentations

Due: January 4, 2023

Each project group will have ~10-12 mins to present their projects in class. The suggested outline for the presentations are as follows:

• High-level overview of the paper (main contributions)
• Problem statement and motivation (clear definition of the problem, why it is interesting and important)
• Related work
• Key technical ideas (overview of the proposed deep model)
• Experimental set-up (datasets, evaluation metrics, applications)
• Strengths and weaknesses (discussion of the results obtained)

Final Report

Due: January 20, 2023 (11:59pm) (No late submissions)

As the last deliverable of the course project, each group is expected to submit a project report prepared using this LaTeX template. The report should be 8 pages and should be structured as a research paper. It will be graded based on clarity of presentation and technical content. A typical organization of a report might follow:

• Title, Author(s).
• Abstract.
• Introduction. This section introduces the problem that you investigated by providing a general motivation and briefly discusses the approach(es) that you explored to solve this problem.
• Related Work. This section discusses relevant literature for your project topic.
• The Approach. This section gives the technical details about your project work. You should describe the representation(s) and the algorithm(s) that you employed or proposed as detailed and specific as possible.
• Experimental Results. This section presents some experiments in which you analyze the performance of the approach(es) you proposed or explored. You should provide a qualitative and/or quantitative analysis, and comment on your findings. You may also demonstrate the limitations of the approach(es).
• Conclusions. This section summarizes all your project work, focusing on the key results you obtained. You may also suggest possible directions for future work.
• References. This section gives a list of all related work you reviewed or used.