DeepLabCut – markerless pose estimation of user-defined features with DL for all animals, including humans
— ML Review (@ml_review) August 5, 2019
By @DeepLabCut @trackingplumes
Projecthttps://t.co/vwXFUM09hb
Githubhttps://t.co/njC4qYy6l6 pic.twitter.com/r1ZUqJmdXN
OpenKiwi is a framework for Machine Translation Quality Estimation from @Unbabel
— PyTorch (@PyTorch) August 2, 2019
It just won the Best Demo Paper award at @ACL2019_Italy , congrats to the team!
Blog Post: https://t.co/UqJLicBOsJ
Get Started: https://t.co/p7dg0dbSg5 pic.twitter.com/bwVwsZNexz
roberta = torch.hub.load('pytorch/fairseq', 'roberta.large') https://t.co/Sdc5UADqDY
— PyTorch (@PyTorch) July 30, 2019
BiGAN implementation in Keras to detect similarities between images of landscapes https://t.co/n8z4WYvn1Q
— François Chollet (@fchollet) July 28, 2019
Here's the link to their adversarial dataset for argument comprehension. Would've been nice (and made the paper more interesting) if they also provided more interesting adversarial examples than just one (about Google 😉), perhaps in the Appendix section. https://t.co/WUhuTR0gq6
— hardmaru (@hardmaru) July 22, 2019
Microsoft open-sources scripts and notebooks to pre-train and finetune BERT natural language model with domain-specific texts: https://t.co/JRe0e6joP2
— David Smith (@revodavid) July 17, 2019
Nice implementations and visual examples of the powerful new CutMix and RICAP augmentation methods, along with cutout and MixUp exampleshttps://t.co/Vy8ghq8zK9 pic.twitter.com/oP0SSYlI7g
— Jeremy Howard (@jeremyphoward) July 15, 2019
Sparse Networks from Scratch: Faster Training without Losing Performance
— ML Review (@ml_review) July 15, 2019
By @Tim_Dettmers @lukezettlemoyer
SoTA sparse performance on MNIST, CIFAR-10, and ImageNet
Increases training speed up to 11.85x
ArXivhttps://t.co/UFtSGjXqLC
Codehttps://t.co/88fHqSsVzN pic.twitter.com/1WCaiNhsVQ
Code from my lightning talk: ensemble topic modelling in Python with pLSA for fast stable topic modelling with the enstop package: https://t.co/fXTHqwbWIu #SciPy2019
— Leland McInnes (@leland_mcinnes) July 12, 2019
I am soon presenting "Building and Benchmarking Automatic Machine Learning Systems" at #useR2019 in the Cassiopée room. 👩🏫 #AutoML @open_ml
— Erin LeDell (@ledell) July 11, 2019
📒 Slides: https://t.co/4KOmdcLjA5
📰 "An Open Source AutoML Benchmark" paper: https://t.co/QAzXgLlwhG
👩💻 Github: https://t.co/XfoPSIKNvU pic.twitter.com/Na2P5VIgC4
Introducing {poissoned} - poisson disc sampling in #RStats.
— mikefc (@coolbutuseless) June 24, 2019
Key feature: produces point sets which are tileable!
Github: https://t.co/yh0HJyBlhI
Post: https://t.co/zntGoW4P1H
Inspiration from @W_R_Chase {poissondisc} 📦 https://t.co/hCjdcpV4Q6 pic.twitter.com/9E48mxEw5z
Exploring Model-based Planning with Policy Networks@TingwuWang and Jimmy Ba
— Brandon Amos (@brandondamos) June 24, 2019
Paper: https://t.co/hXOUZ7OCTG
Code: https://t.co/HUKfhV635h pic.twitter.com/Yis26OpXyx
