For those who still don’t understand how wonderfully the vaccines are protecting us from dying of COVID. It’s mostly the unvaccinated minority that’s dying of COVID. This is CDC data. Source: https://t.co/42sRuPIjsg pic.twitter.com/3DKYVgzptB
— Simon Kuestenmacher (@simongerman600) January 13, 2022
Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents
— AK (@ak92501) January 13, 2022
abs: https://t.co/ehdybJKTr5
project page: https://t.co/HnfvJnYcSM pic.twitter.com/6Q2vHRfCZm
For the same reason therapy is hard. https://t.co/YisEFvmORc
— francesc (@francesc) January 13, 2022
PromptBERT: Improving BERT Sentence Embeddings with Prompts
— AK (@ak92501) January 13, 2022
abs: https://t.co/N1EQlKRJAi
github: https://t.co/hrTlbFFks4
Compared to SimCSE, achieve 2.29 and 2.58 points improvements on BERT and RoBERTa respectively under
the unsupervised setting pic.twitter.com/UkhC9F6jTq
The Annotated S4 (https://t.co/Qyylpwo6J6 /w @siddkaramcheti)
— Sasha Rush (@srush_nlp) January 12, 2022
A step-by-step guide for building your own 16,000-gram language model... pic.twitter.com/tcVac4pSgd
The term "ablation" is widely misused lately in ML papers. An ablation is a removal: you REMOVE some component of the system (e.g., remove batchnorm). A "sensitivity analysis" is where you VARY some component (e.g., network width). #pedantic
— Thomas G. Dietterich (@tdietterich) January 11, 2022
Data-Efficient Information Extraction from Form-Like Documents
— AK (@ak92501) January 11, 2022
abs: https://t.co/DuvnesJPpl pic.twitter.com/Y7WMkG81xj
QuadTree Attention for Vision Transformers
— AK (@ak92501) January 11, 2022
abs: https://t.co/ImFIQAQsZn
4.0% improvement in feature matching on ScanNet, about 50% flops reduction in stereo matching, 0.4-1.5%
improvement in top-1 accuracy on ImageNet classification, 1.2-1.8% improvement on COCO object detection pic.twitter.com/0yxa0VnMCO
I’ve been using #AnnotatedEquations in my recent papers. I think it really adds to the readability and understanding of the math.
— Sibin (@sibinmohan) January 10, 2022
Here are some examples. It uses #tikz in #latex.
Let me know if you like it. Happy for any feedback. [GitHub link: next tweet]#AcademicChatter
+ pic.twitter.com/QJrm2Wbvyy
If you'd like to learn the @JuliaLanguage and you know Python, then this tutorial Colab notebook is for you. I just updated it to the latest Julia 1.7.1 version:https://t.co/0ljztliJNv
— Aurélien Geron (@aureliengeron) January 9, 2022
Enjoy! 🙂
deepchecks - Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort. See our docs: https://t.co/JPGQK3lRr0 https://t.co/XjDOyKNown
— Python Trending (@pythontrending) January 8, 2022
Bravo.. This is super effective imo! https://t.co/y2hUmvGkuF
— Hilary Parker (@hspter) January 8, 2022
