Poster Presentations
Most computer science conferences include a poster session where presenters stand beside large-format posters that summarize research results. Conference attendees then have the opportunity to sip beverages while circulating among the posters and talking to the presenters about their work.
The goal of our virtual poster session is to give you a chance to research some area of machine learning that interests you, and to present what you have learned. I'll be flexible about the topics of the posters. Possibilities include:
- A machine learning application area.
- A machine learning algorithm that we did not get a chance to cover in detail.
- A machine learning project that you are proposing - possibly with the first steps completed.
Your topic should be narrow enough for you to present it in some depth. Your poster should be visually appealing, include appropriate figures, and be technically accurate. Your poster should include properly formatted references to peer-reviewed papers.
You will get a deeper understanding of your topic if you dive in and actually write some code or try out some existing tools. A small component of your grade will be based on presenting original results. These results need not be groundbreaking or publishable. You should clearly establish an experimental question and show results that address that question.
There will be four deadlines for this project:- Deadline 1: Topic proposal + preliminary bibliography.
- Deadline 2: Annotated bibliography.
- Deadline 3: Electronic poster submission.
- Deadline 4: Poster presentation.
Topic Proposal
For this deadline you should submit a short (1-2 paragraph) description of your topic, along with at least three representative references. I suggest that you follow APA style in formatting your references. I won't be picky about formatting, but you should be consistent and you must provide complete bibliographic information.
Note that a URL is not sufficient. At the least, your references must include the name of the journal or conference, the names of the authors and the original publication date.
Annotated Bibliography
Your annotated bibliography must include at least five peer-reviewed conference or journal papers. At least three of these papers must contain a reference to some other paper in your bibliography. At least three of your papers must have been published within the last four years. For each paper, you must provide complete bibliographic information as well as a brief summary of the paper. The summary should describe the key results, note any references to other papers in your bibliography, and explain the connection to those referenced papers. The target audience for these summaries should be other students in CS 445.
You should make your best effort to understand the content of the papers, but you should also make it clear if there are elements of the paper that you did not understand.
I won't be picky about the overall formatting, but make sure to include your name, a title, etc. Here is an OverLeaf template that you could use as a starting point: Annotated Bibliography Template.
You may use AI tools to help understand your papers, but the use of AI tools in generating the text of your summaries will be considered an honor-code violation.
Tools
Tools like Zotero and Mandely can be very helpful for storing and organizing references. These tools are also useful for citation formatting.Poster
The poster should be landscape 36"x24" (3 feet wide and 2 feet tall). Here is a link to some slides with good advice on preparing a scientific poster:
When planning your poster, imagine it as a sequence of slides for a 7-10 minute presentation on your topic. However, keep in mind that a poster differs from a PowerPoint presentation in terms of the amount of text that is appropriate:
- PowerPoint Presentations: Slides should have minimal text to encourage the audience to listen rather than read. The slides serve mainly as visual aids—such as graphs and diagrams—to support your presentation.
- Posters: While a poster should also avoid large blocks of text, it should be possible to understand the key ideas even if you aren't there to explain them. This means you can include slightly more text than in slides, but it should still be concise and easily digestible at a glance. You should use short, clear sentences and prefer bullet points over paragraphs.
Finding Papers
Here are some possible starting points for finding high quality papers.
Google Scholar is probably your best starting point. A good way to get started is do some keyword searches related to your topic and take a look at the most highly cited papers that appear relevant. There are many low-quality or uninteresting papers out there. Citation counts provide a good mechanism for focusing attention on noteworthy papers. Once you find an interesting paper you can follow forward and backward citations to get a deeper understanding of the topic.
Conference publications are the main avenue for disseminating research results in computer science. I've highlighted a few of the top conferences in several AI areas below. Except where noted, the proceedings for these conferences should be available on-line.
First-tier Conferences:
- Machine Learning:
- General AI (Many, but not all, of the papers in these venues will be focused on machine learning):
First-tier Journals
A Note on arXiv.org
The arXiv.org web site provides a popular avenue for quickly disseminating research results that may not have undergone a formal process of peer review. Sometimes arXiv papers are under review for a journal or conference, sometimes they are pre-publication versions of papers that have since appeared in a peer-reviewed publication, sometimes they are more like informal white papers that are not intended for formal peer review.
The arXiv is great, but for the purposes of this project you should only use arXiv papers if they have actually appeared in a peer reviewed publication, or if they are clearly seminal papers (hundreds of citations). Full publication information may appear in the "comments" section of a paper's arXiv page. The reference information you provide must include the full publication information.
Reading Research Papers
Reading a research paper is not like reading a novel or even a textbook. Research papers are usually written under the assumption that the audience will be other researchers in the same field. In addition, papers are often written under strict page limits that restrict amount of background information the authors can provide. The keys to making sense of research papers are patience and perseverance. I suggest the following steps.
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Start by reading the abstract, the introduction and the conclusion. At this point the goal is to figure out the big-picture claims that the authors are making. What have they accomplished? Why does it matter? At this stage you may determine that the paper is not worth reading. If so, move on to a different paper.
- Next, read the whole paper all the way through. Twice. Read carefully, but don't get bogged down by equations or details. Get a sense for the overall organization of the paper and the main points that are being made in each section. Underline or highlight points that seem particularly important or difficult to understand.
- Finally, go back and try to puzzle out the parts of the paper that were unclear on the first few read-throughs. You will probably find that there are terms or concepts that are important, but are not explained in the paper. The Internet/Wikipedia may be helpful. You may also find that the authors are assuming that you are familiar with some other paper or body of work. You obviously won't be able to read every cited paper (and all of the papers cited in those papers), but some extra reading may be unavoidable.
By the time you finish, you should understand the key points that are being made in the paper. You may not understand every sentence and every equation, but you should know what you don't know, and be in a position to discuss it.
Grading
The grade for this project will be calculated as follows:
| Proposal | 10% |
| Bibliography | 20% |
| Poster Presentation | 50% |
| Your Evaluations | 10% |
| Original Results | 10% |
Your poster presentation will be evaluated by me, as well as by other members of the class, and possibly other members of the department. You will be evaluated both on the poster itself, and on your ability to present the contents.
Acknowledgments
This project is based on a similar project developed by George Ferguson at the University of Rochester.