We’ve discussed presentation preparation before, but I have one more thing to add: transitioning. For a research presentation, it is substantially helpful for the audience if transitions are clear. A common outline for a research presentation in machine leanring is:
- The problem. Presentations which don’t describe the problem almost immediately lose people, because the context is missing to understand the detail.
- Prior relevant work. In many cases, a paper builds on some previous bit of work which must be understood in order to understand what the paper does. A common failure mode seems to be spending too much time on prior work. Discuss just the relevant aspects of prior work in the language of your work. Sometimes this is missing when unneeded.
- What we did. For theory papers in particular, it is often not possible to really cover the details. Prioritizing what you present can be very important.
- How it worked. Many papers in Machine Learning have some sort of experimental test of the algorithm. Sometimes this is missing when the work is theoretical.
What seems to often happen, is that there is no transitioning in the presentation. This can happen in one of two ways:
- Content Confusion. Sometimes the problem description is merged into (2), and (3). Sometimes (2) and (3) are merged. When this happens, it can be very difficult to follow. The solution is to rewrite to isolate the presentation components.
- Untransition. Sometimes the presentation does have a reasonable structure as above, but there are just no transitions in the delivery, creating apparent content confusion. This is easy to fix. An approach I often use is to just have an outline slide with the next subject highlighted between pieces of the transition. The delivery of the presentation can also handle this well. For example, have an extra long pause after stating the problem and check to see if the audience has questions.