Congratulations to Yuyu Zhang, Hanjun Dai, Kamil Toraman, & Le Song on achieving a new highest score of 31.7% on the AI2 Reasoning Challenge (ARC) dataset! Can you top that? 📈 Learn more on the ARC webpage: https://t.co/rgQx5NynIu #AI2 pic.twitter.com/Q0lllcOzxI
— Allen Institute for Artificial Intelligence (AI2) (@allen_ai) June 21, 2018
Did you know that Dropout was originally introduced in a Master's thesis and was rejected from NIPS? Was disseminated via #arxiv! #OHBM2018 pic.twitter.com/DHYIAVvKCe
— Chris Gorgolewski (@ChrisFiloG) June 21, 2018
Launched a new website, https://t.co/WDCq9K3lwv, dedicated to giving deep but intuitive explanations of machine learning and related topics.
— Terence Parr (@the_antlr_guy) June 21, 2018
An overview of the work being done in the bias in ML area, featuring quotes from @achould @dgrobinson @katecrawford @geomblog @s010n @vmsoutherland. https://t.co/WpkEBMkj8f
— Kristian Lum (@KLdivergence) June 20, 2018
Will deep learning revolutionize clinical predictions from EHRs? Excellent sleuthing by @ShalitUri shows that recent Google AI work on this isn't any better than (simpler, fundamentally more interpretable) regularized logistic regression. @GaryMarcus @filippie509 https://t.co/VkeBzCyL7Z
— Max Little (@MaxALittle) June 20, 2018
Nice thoughts on the "Learn Python challenge on Kaggle" from one of our users https://t.co/s7bDUViPXX pic.twitter.com/3zAO1VF6oF
— Ben Hamner (@benhamner) June 20, 2018
Probability of losing money in the #StockMarket based on the number of years you remain invested in a stock. #investing #dataviz
— Randy Olson (@randal_olson) June 20, 2018
Details here: https://t.co/zGUEiXWDEk pic.twitter.com/gj9S6Unoy2
That footnote :) pic.twitter.com/9WJ3JMHEnF
— Ishaan Gulrajani (@__ishaan) June 20, 2018
#AIStartup @qure_ai is changing the way doctors prioritize brain injuries through #GPU-accelerated #deeplearning technology. Learn how it detects critical problems in head CT scans in less than 10 seconds: https://t.co/brMUB4iRzO pic.twitter.com/yKz0rzO7dF
— NVIDIA AI (@NvidiaAI) June 20, 2018
Projects from @Stanford's CS230 (Deep Learning) class. Congrats to all the students on completing so many great projects! https://t.co/5Q4d4afrl0
— Andrew Ng (@AndrewYNg) June 20, 2018
Large samples let you focus on effect size - it means you probably have a good estimate of this and now need not worry about noise/sampling variability. This is *always* a good thing. How does this large sample myth persist? https://t.co/qujI6TkqIe
— Manjari Narayan (@NeuroStats) June 20, 2018
Deep Learning; The study of neural networks and the art of making all kinds of stuff differentiable (including things that really shouldn’t be)
— Denny Britz (@dennybritz) June 19, 2018
