Winning entry of the 2019 video activity detection challenge now open source. https://t.co/x3PrTcRyV6
— Yann LeCun (@ylecun) October 29, 2019
Winning entry of the 2019 video activity detection challenge now open source. https://t.co/x3PrTcRyV6
— Yann LeCun (@ylecun) October 29, 2019
We’re introducing a new framework, called TensorMask, that uses a dense, sliding-window technique for very sharp instance segmentation. Read more: https://t.co/AUFcRFaBZm #iccv2019 pic.twitter.com/8Mp3VuhTqg
— Facebook AI (@facebookai) October 29, 2019
Stoked to share a milestone project for all of us! #NeurIPS2019 paper with @akshaykagrawal, @ShaneBarratt, S. Boyd, S. Diamond, @zicokolter:
— Brandon Amos (@brandondamos) October 28, 2019
Differentiable Convex Optimization Layers
Paper: https://t.co/FZnyvjObiD
Blog Post: https://t.co/BJcZasljk3
Repo: https://t.co/eaE0j7mccy https://t.co/8P4Xfz1fug
How can computers answer questions with multi-step information needs? How can it be done efficiently and interpretably? @qi2peng2 and colleagues explain at #emnlp2019. Paper: https://t.co/lxC4ul6rC6 Blog post: https://t.co/h4Z1xTXrVd #NLProc
— Stanford NLP Group (@stanfordnlp) October 27, 2019
Does #deeplearning "understand" natural language? Has common sense been solved? Try for yourself at https://t.co/8LkSUMTrDv, from @AdamDanielKing.
— Gary Marcus (@GaryMarcus) October 26, 2019
Here's a sample from this morning, my question in bold, transformer network answer at bottom, starts "It's the same as... " pic.twitter.com/SMd8VDLh0h
Development and validation of deep learning algorithms for scoliosis screening https://t.co/E0sLgrJCYC
— Nando de Freitas (@NandoDF) October 25, 2019
text-to-text-transfer-transformer - https://t.co/ts5xMqoo9q
— Python Trending (@pythontrending) October 25, 2019
1/3 This morning I presented at #ieeevis the work on a super-fast #tSNE developed during my internship at @GoogleAI with @zzznah. Slides, demo and code are available at https://t.co/kZqXrU4R7M #IEEEVIS2019 @tudelft @BoardTUe @EEMCS_TUD @Google
— Nicola Pezzotti (@nicolapezzotti) October 24, 2019
🚨🚨Big #nlproc claim from Google: "we .. [introduce] a unified framework that converts every language problem into a text-to-text format." https://t.co/znJ5DefCoG pic.twitter.com/pBZrdompF9
— Delip Rao (@deliprao) October 24, 2019
GDPR requires active consent for tracking. Most websites have pre-selected boxes, which is not allowed. What % of users would actively accept tracking to get the supposed benefits of personalization? 50%? 25%?
— Arvind Narayanan (@random_walker) October 24, 2019
A new study found it's about ONE FIFTH OF 1%. https://t.co/r2qK5sTaSt pic.twitter.com/iv2Xgph9jE
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer https://t.co/1oyuqNC8hG
— /MachineLearning (@slashML) October 24, 2019
One year ago, @seb_ruder asked the https://t.co/GEOZuodrZj community for help with multilingual language modeling.@eisenjulian @PiotrCzapla @misterkardas answered the call, and we now have MultiFiT, an EMNLP paper & code for multilingual training!https://t.co/FwY6QGV0gA pic.twitter.com/JYQRpz8wHT
— Jeremy Howard (@jeremyphoward) October 23, 2019