[Thread on #MachineLearning from #streaming #data]

Announcing our (@matthewnokleby, @haroonraja86, and myself) paper on "Scaling-up Distributed Processing of Data Streams for Machine Learning", which has been accepted by @ProceedingsIEEE (Preprint: arxiv.org/abs/2005.08854).

1/ It focuses on training of models from fast streaming data, with "fast" referring to the inability of a single machine to process each data sample in time before the next one arrives. Distributed training can help deal with this, but how many nodes, what minibatch size, etc.?

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[Thread on #remoteteaching]

Thank you @ProfJohnRBuck for a wonderful convo on fall remote teaching. I always learn so much from our discussions and the call today was no exception! For the benefits of others, here are a few things that came up in the call. #AcademicTwitter

1/ What is the best way to develop a personal connection with students, when we won't be seeing each other face to face ever. It's different than in spring, when we started off with face-to-face instructions. This requires some serious thinking. #onlinelearning #remotelearning

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[Thread on reviewing for #MachineLearning confs, after receiving the reminder from @iclr_conf (#ICLR)]

Posting response to @iclr_conf's request for reviewing here in the hope (again) that we can change the reviewing structure of ML conferences to promote better science.

1/ Whether it is @NeurIPSConf (#neurips), @icmlconf (#icml), @iclr_conf (#ICLR), @RealAAAI (#aaai) or any other crowded ML conf, the reviewing structure that involves a fixed review window, multiple assigned papers, unlimited supplemental material, etc., promotes the following:

2/