Learning the internals of Machine Learning systems and tips for PhD | Maithra Raghu, Google Brain

Jay Shah Podcast

Dr. Maithra Raghu is a senior research scientist at Google working on analyzing the internal workings of deep neural networks so that we can deploy them better keeping humans in the loop. She recently graduated from Cornell University with a PhD in CS and previously graduated from Cambridge University with BA and Masters in Mathematics. She has received multiple awards for her research work including the Forbes 30 under 30.
Questions that we cover
00:00:00 Introductions
00:01:00 To understand more about your research interests, can you tell us what kind of research questions you are interested in while working at Google Brain?
00:04:45 What interested you about it and how did you get started?
00:15:00 What is one thing that surprises/puzzles you about deep learning effectiveness to date?
00:22:05 What’s the difference between being a researcher in academia/PhD student vs being a researcher at a big organization (Google)?
00:28:35 In what use cases do you think ViTs might be a good choice to perform image analysis over CNN vs where do you think CNNs still have an undoubted advantage?
00:37:15 Why does ViT perform better than ResNet only on larger datasets and not on mid-sized datasets or smaller?
00:43:55 In regards to medical imaging tasks, would it be theoretically wrong to pre-train the model on dataset A and fine-tune it on dataset B?
00:47:35 Do you think ViT or transformer-based models already have/have the potential to cause a paradigm shift in the way we approach imaging tasks? Why?
00:5:25 Medical datasets are often limited in size, what are your views on tackling these problems in the near future
00:55:55 From an internal representation perspective, do you think deep neural networks can have the ability of reasoning?
00:58:20 How did you decide on your own PhD research topic? Advice you would give to graduate researchers trying to find a research problem for their thesis?
01:04:00 Many times researchers/students feel stuck/overwhelmed with a particular project they are working on, how do you suggest based on experience to tackling that?
01:10:35 How do you now/as a graduate student used to keep up with the latest research in ML/DL?
Maithra's Homepage: https://maithraraghu.com
Blogpost talked about: https://maithraraghu.com/blog/2020/Reflections_on_my_Machine_Learning_PhD_Journey/
Her Twitter: https://twitter.com/maithra_raghu
About the Host:
Jay is a PhD student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
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