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3rd workshop on Artificial Intelligence Diversity, Belonging, Equity, and Inclusion (AID BE I) at #AAAI: a livetweet thread by @banazir
#DiversityInAI #DiverseInAI
@RealAAAI @WiMLworkshop @black_in_ai @_LXAI @QueerinAI @AiDisability #IndigenousInAI
@wimlds @BlackWomenInAI
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#DiversityInAI #DiverseInAI
@RealAAAI @WiMLworkshop @black_in_ai @_LXAI @QueerinAI @AiDisability #IndigenousInAI
@wimlds @BlackWomenInAI
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Welcoming remarks from @banazir:
There will again be a special issue of Proceedings of Machine Learning Research (#PMLR, an imprint of @JmlrOrg's Journal of #MachineLearning #research) on this workshop.
Video recordings of the workshop will be at DiverseInAI.org.
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There will again be a special issue of Proceedings of Machine Learning Research (#PMLR, an imprint of @JmlrOrg's Journal of #MachineLearning #research) on this workshop.
Video recordings of the workshop will be at DiverseInAI.org.
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First presentation: "Hello* - A Beginner's Guide to the Conference Galaxy" - Bethany Chamberlain, Dovile Juodelyte and Veronika Cheplygina
@chamberlain_ba @DrVeronikaCH
Great use of @Mentimeter!
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@chamberlain_ba @DrVeronikaCH
Great use of @Mentimeter!
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π’ In our #ACMMM21 paper, we highlight issues with training and evaluation of π°πΏπΌππ± π°πΌππ»ππΆπ»π΄ deep networks. π§΅π
For far too long, π°πΏπΌππ± π°πΌππ»ππΆπ»π΄ works in #CVPR, #AAAI, #ICCV, #NeurIPS have reported only MAE, but not standard deviation.
Looking at MAE and standard deviation from MAE, a very grim picture emerges. E.g. Imagine a SOTA net with MAE 71.7 but deviation is a whopping 376.4 !
[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.
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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.
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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:
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a) Reviews are handed off to inexperienced reviewers, directly or indirectly, due to the time pressure involved.
b) It leaves reviewers with little time to digest the technical details, leading them to potentially miss subtle errors or undervalue the merits of the work.
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b) It leaves reviewers with little time to digest the technical details, leading them to potentially miss subtle errors or undervalue the merits of the work.
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