Data Science by ODS.ai 🦜
51K subscribers
363 photos
34 videos
7 files
1.52K links
First Telegram Data Science channel. Covering all technical and popular staff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former. To reach editors contact: @haarrp
Download Telegram
​​🤖Handl: New dataset labeling tool release

Handl is a tool to label and manage data for machine learning. It employs 25k qualified crowdworkers who help tech companies to deal with data preparation and get paid for it. Consensus algorithm ensures the quality of labeling for any type of data — images, texts, and sounds.
#Handl was released today at Product Hunt, so developers might benefit from community upvotes, please consider supporting such useful tool on Product Hunt.


Link: https://handl.ai
Product Hunt url: https://www.producthunt.com/posts/handl-3

#handl #machinelearning #ai #data #datalabeling
On the concept of 'intellectual debt'

There is technical debt — when you know you should rewrite some stuff, or implement some features, but they don't seem critical at the moment. So article introduces a concept of 'intellectual debt', which resies with more broad and common use of #MachineLearning and #DeepLearning (specially, the latter). What happens when AI gives us seemingly correct answers that we wouldn't have thought of ourselves, without any theory to explain them?

Link: https://www.newyorker.com/tech/annals-of-technology/the-hidden-costs-of-automated-thinking

#Meta #common #lyrics
​​Model for tweaking graph visualization layout parameters

New #MachineLearning model builds a WYSIWYG interface to intuitively produce a layout you want!

Demo: http://kwonoh.net/dgl
Paper: http://arxiv.org/abs/1904.12225

#Visualization #ML
​​ReBotNet: Fast Real-time Video Enhancement

The authors introduce a novel Recurrent Bottleneck Mixer Network (ReBotNet) method, designed for real-time video enhancement in practical scenarios, such as live video calls and video streams. ReBotNet employs a dual-branch framework, where one branch focuses on learning spatio-temporal features, and the other aims to enhance temporal consistency. A common decoder combines the features from both branches to generate the improved frame. This method incorporates a recurrent training approach that utilizes predictions from previous frames for more efficient enhancement and superior temporal consistency.

To assess ReBotNet, the authors use two new datasets that simulate real-world situations and show that their technique surpasses existing methods in terms of reduced computations, decreased memory requirements, and quicker inference times.

Paper: https://arxiv.org/abs/2303.13504
Project link: https://jeya-maria-jose.github.io/rebotnet-web/

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-rebotnet

#deeplearning #cv #MachineLearning #VideoEnhancement #AI #Innovation #RealTimeVideo