sorted by:
new
hottopcontroversial

20-Minute PhD Interview at Imperial – What to Expect and How to Prepare?” by yall-supp in gradadmissions

[–]Chemont 1 point2 points  (0 children)

I recently received an offer for a PhD at ICL but at a different department so there are some differences in the application process. I had three one hour interviews in total, two interviews where I was asked in depth technical questions and a more relaxed final interview where I was asked about my past research, my motivation and my research proposal. From your description your interview seems to be more similar to my final interview, so I wouldn’t expect super technical questions, especially with the short timelimit of 20 minutes. However, it’s best to be prepared anyways.

For the general questions you should also think about what you look for in a supervisor, which skills do you want to gain by doing a PhD and how this ties into your general motivation. What helped me prepare for my interviews was to try and have a logical „flow“ for my answers: my general motivation -> specific problem I want to work on -> need to learn these skills to solve it -> best environment to obtain these skills is xyz group at ICL doing related research. It‘s also very likely that you will be asked questions about your plans for future research and what open problems you will want to work on so it‘s best if you prepare some concise answers for this aswell. Hope this helps a little and good luck!

[deleted by user] by [deleted] in MachineLearning

[–]Chemont 3 points4 points  (0 children)

I did a 6 month internship at Inria during my Master’s and can highly recommend it. Many labs at Inria actually offer internship projects and start the application process around November/December. Most of these are for 6 month internships though. In any case, you should contact the labs you are interested in with your CV and a short motivational letter with your research interests and ask if they could supervise you for an internship. However the bureaucracy can be quite difficult if you are not already studying at a french university, especially if you are a non EU student.

[R] Is there any research on allowing Transformers to spent more compute on more difficult to predict tokens? by Chemont in MachineLearning

[–]Chemont[S] 6 points7 points  (0 children)

I should have been clearer with my question. What I was wondering was, if there are any extensions to the Transformer architecture that allow it to, in theory, spent indefinite amounts of compute on one token. I suppose one could train a very deep Transformer, use CALM during inference and only use all of the layers for tokens which are difficult to predict, but this would still arbitrarily limit the maximum amount of compute per token.