The technical round with research scientist. I was asked some research questions on diffusion policy, RL algorithms, adv and disadvantages of different types of action space in manipulation. How to go beyond data scaling? And few more questions on manipulation and mobile manipulation state of arm algo.
Which ensemble learning method, random forest or gradient boosting, has more bias and which one has more variance?
Discuss ideas for improving the performance of a machine translation system. Followup, how would you decide whether you succeeded?
Explain what V K Q mean in transformer models
What is difference between DNS and RANS in CFD
Find the longest path in a graph
How to build a recommender system from product description. What is SGD and why and when does it work, and how to improve.
Cross validation, precision, recall, parallel computing, algorithm questions. No OR questions.
If you didn't have a molecular engine, how would you build one?
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