Visualization
Height distribution by position of the players in the #WorldCup2018. #datavizhttps://t.co/W4rZAHIPaB #FIFAWorldCup pic.twitter.com/c9tETbi0fh
— Randy Olson (@randal_olson) June 20, 2018
If we all left to "go back where we came from" there wouldn't be many people in the US https://t.co/Xm3k9DKIQY pic.twitter.com/8V28jjFedc
— Nathan Yau (@flowingdata) May 16, 2018
My favorite thing about #datavisualization is when someone actively refuses to learn how to read a chart and then throws their arms in the air (like one of those As Seen on TV commercials where they can't use a cup or a sock) and declares it unreadable.
— Elijah Meeks (@Elijah_Meeks) June 20, 2018
NLP Decathlon
Very excited to announce the natural language decathlon benchmark and the first single joint deep learning model to do well on ten different nlp tasks including question answering, translation, summarization, sentiment analysis, ++https://t.co/R5wbnAQcC3 pic.twitter.com/4fotVhdRow
— Richard (@RichardSocher) June 20, 2018
All the code has been open sourced at https://t.co/KSp2pOdQNv
— Bryan McCann (@BMarcusMcCann) June 20, 2018
The Natural Language Decathlon: Great to see more research on multi-task / transfer learning coming out of Salesforce Research! Cool that casting everything as QA works so well. https://t.co/v6PSctxmS4
— Sebastian Ruder (@seb_ruder) June 20, 2018
Natural Language Decathlon (decaNLP) — a test for multi-task NLP
— Mark Riedl ✈️ SFO (@mark_riedl) June 20, 2018
Blog: https://t.co/wXbDvDcKLh
Paper: https://t.co/sGBOv4tqEx
Code: https://t.co/D3gphpJvLn pic.twitter.com/bcPW8g8Rel
Virtual Creatures Contest
Virtual Creatures Contest @GECCO2018. Submit a short video of your artificial creatures by June 29! https://t.co/jnsPBYreWs pic.twitter.com/0uotHcpugu
— hardmaru (@hardmaru) June 20, 2018
Notable Research
Our paper "Differentiable plasticity: training plastic networks with backpropagation" was accepted at ICML 2018!
— Thomas Miconi (@ThomasMiconi) June 20, 2018
Check out the updated version, now with code snippets - make your RNNs plastic with 4 lines of code!https://t.co/j5TrbRvbuB pic.twitter.com/8SaQDhl2rL
ChoiceNet
Check our recent work on robust deep learning with a new architecture named ChoiceNet. Just uploaded TF implementations and some results. https://t.co/kk1Gi7R1IH pic.twitter.com/yS7fi9Gs2p
— Sungjoon (@sj_sam_choi) June 18, 2018
Tutorials and Resouces
The smartest code I've ever put at the bottom of a Jupyter Notebook. pic.twitter.com/n0MTdPh4W2
— Chris Albon (@chrisalbon) June 20, 2018
Today at #CVPR2018 we introduce @NVIDIA DALI & nvJPEG, new #deeplearning libraries for data augmentation and image decoding. https://t.co/nndrnQBG1a pic.twitter.com/OAvs7ixpMl
— NVIDIA AI Developer (@NVIDIAAIDev) June 19, 2018
I'm happy to announce the first release of ignite - a high level library for @pytorch helping you write compact but full-featured training loops in a few lines of code! Check it out at https://t.co/d9BBOEXMXF
— Alykhan Tejani (@alykhantejani) June 20, 2018
I just published "How to build a Teachable Machine with TensorFlow.js" using transfer learning and k-nearest neighbors, a new interactive @observablehq tutorial and notebook.
— Nikhil Thorat (@nsthorat) June 20, 2018
We also released a small KNN TensorFlow.js utility with the notebook!https://t.co/DXRDMnbZJd pic.twitter.com/rnVd28lftc
new blog post: Slides from "How Stan computes the posterior distribution" https://t.co/hqkhRcUSm1
— Daniel Lee (@djsyclik) June 20, 2018
rstats
📊 Easy viz from #rtweet data!
— Mara Averick (@dataandme) June 20, 2018
🐦 "GraphTweets: Visualise Twitter Interactions" ✩ @jdataphttps://t.co/SCN0Ycy7Z7 #rstats #dataviz pic.twitter.com/YaPUkOLcme
Many of my stats337 students (readings in applied data science) were happy to share their final annotated bibliographies: https://t.co/EbrQorNt5l — some great suggested readings on a selection topics!
— Hadley Wickham (@hadleywickham) June 20, 2018
Why {tidyverse} 📦 ❌ dependencies…
— Mara Averick (@dataandme) June 20, 2018
⚠️ "The tidyverse is for EDA, not packages" https://t.co/soP4kVTjeJ #rstats #tidyverse
Miscellaneous
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
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
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
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
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
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
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
Someone told me early on in my career:
— Dr. Yana Weinstein (@doctorwhy) June 16, 2018
This isn’t some kind of magical higher calling. It’s just a job like any other. You’re a cog in the university’s machine, and you’re replaceable.
I didn’t believe it until I experienced it for myself.
Don’t give too much to the machine.
Launched a new website, https://t.co/WDCq9K3lwv, dedicated to giving deep but intuitive explanations of machine learning and related topics.
— The ANTLR Guy (@the_antlr_guy) June 21, 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
Just a reminder that there is a strong and growing #AI #ethics crowdsourced document that's meant for all levels :) https://t.co/MpQTNTJqIk
— Rumman Chowdhury (@ruchowdh) June 20, 2018
That footnote :) pic.twitter.com/9WJ3JMHEnF
— Ishaan Gulrajani (@__ishaan) June 20, 2018