15 curated AI reads for August 2018
August, 2018
News about Machine Learning (ML), Artificial Intelligence (AI), Data Science (DS) and related advanced analytics areas.
Welcome to xplore.ai’s second post of the 15 curated AI reads monthly series. The objective of these series is to provide the audience with a curated list of the most interesting news, publishings and tools that our team have ran into during the previous month.
15. ✏️ Agile methodologies in Data Analytics
The good, the bad and the adjustments to make Agile methodologies work in the Data industry:
https://www.locallyoptimistic.com/post/agile-analytics-p1/
https://www.locallyoptimistic.com/post/agile-analytics-p2
https://www.locallyoptimistic.com/post/agile-analytics-p3
14. ✏️ Types of charts, visual vocabulary cheatsheet
Simple and to the point complete cheatsheet of the common types of charts
https://raw.githubusercontent.com/ft-interactive/chart-doctor/master/visual-vocabulary/poster.png
13. 💣Robust ML, adversarial attacks
A community-run hub for learning about robust ML, a rapidly growing field that spans diverse communities across academia and industry. Robust models and precisely specifying the threat models under which they claim to be secure. Also includes interfaces for specifying attacks and evaluating attacks against models.
12. 📄Repo where tracking progress of NLP happens
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks. Really nice to keep an eye on, if you want to be up to date in NLP.
11. ✏️ Cancer Metastasis detection using Neural Conditional Random Field (NCRF)
Breast cancer diagnosis often requires accurate detection of metastasis in lymph nodes through Whole-slide Images (WSIs). Recent advances in deep convolutional neural networks (CNNs) have shown significant successes in medical image analysis and particularly in computational histopathology. Good results are achieved when using neural conditional random field (NCRF). The corresponding paper can be found here.
baidu-research/NCRF
Cancer metastasis detection with neural conditional random field (NCRF)
github.com
10. ✏️ Organized Resources for Deep Learning in Natural Language Processing (NLP)
Yet another set of organized resources for Deep Learning in Natural Language Processing (NLP). Pretty well segmented. Though, more than one month without updates, let’s see if they can keep up to date the info.
9. ✏️ 10 coolest papers from Computer Vision and Pattern Recognition 2018 conference
During 18 jun. 2018–22 jun. 2018, CVPR (Computer Vision and Pattern Recognition) conference took place in Salt Lake City, US. The 10 coolest papers are presented and commented. Seems that the trending topics at the moment are spinning around image segmentation (efficient annotation tools, R-CNN, etc), GAN continues, training with synthetic data, extra slow-mo using interpolation.
8. ✏️ Stockholm’s new promising startup called Peltarion, from ex-engineers at Spotify and King
Senior engineers from a raft of other Swedish tech unicorns — Spotify, Klarna and Truecaller — have joined to create a startup called Peltarion, which has raised $16 million to simplify the usage of off-the-shelf machine-learning tools like Google’s TensorFlow, Amazon’s Sage Maker and Microsoft’s Azure Machine Learning.
7. ✏️ What’s new in YOLO v3?
Improvements from this popular object detection Neural Network. We are still amazed about the performance and smart ideas behind.
6. ✏️ Hierarchical Attention Networks
How to train and visualize Hierarchical Attention Networks?
5. ✏️ World Models
Using Chainer, a powerful, flexible, and intuitive framework for Neural Networks, World Models paper are implemented.
4. 🕺 GAIN (Guided Attention Inference Network)
Contains implementation of Guided Attention Inference Network (GAIN) presented in Tell Me Where to Look (CVPR 2018). This repository aims to apply GAIN on fcn8 architecture used for segmentation.
3. 😀Text to Face
This study aims at transforming an English description of a face to an image, automatically. It combines two of the recent architectures StackGAN and ProGANfor synthesizing faces from textual descriptions.
2. 📈 M4 Forecasting Competition: Introducing a New Hybrid ES-RNN Model
A nice discussion on how Slawek Smyl (Uber) hybrid Exponential Smoothing-Recurrent Neural Networks (ES-RNN) method work. It out-performed many other complex and simple but smart approaches. It mixes hand-coded parts like ES formulas with a black-box recurrent neural network (RNN) forecasting engine. Versions of the ES-RNN model are under development to tackle some of the most challenging problems in time-series forecasting here at Uber, across various use cases.
1. 🌱 Seedbank
Yet another Google technical idea that will help a lot of people find classified cutting-edge examples and snippets on how to apply the latest AI advances with already created Google Colab notebooks.
And this is it for what we found out to be interesting in June. At xplore.ai, we are always trying out the latest tools, experimenting with cutting edge algorithms and reading about the latest trends in every industry where data is generating unprecedented value.
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