Overviews of Graph Classification and Network Clustering methods

Thanks to Sebastian Raschka I am able to share this great GitHub overview page of relevant graph classification techniques, and the scientific papers behind them. The overview divides the algorithms into four groups: Factorization Spectral and Statistical Fingerprints Deep Learning Graph Kernels Moreover, the overview contains links to similar collections on community detection, classification/regression trees and gradient boosting papers…

ArchiGAN: Designing buildings with reinforcement learning

I’ve seen some uses of reinforcement learning and generative algorithms for architectural purposes already, like these evolving blueprints for school floorplans. However, this new application called ArchiGAN blew me away! ArchiGAN (try here) was made by Stanislas Chaillou as a Harvard master’s thesis project. The program functions in three steps: building footprint massing program repartition…

Free Programming Books (I still need to read)

There are multiple unread e-mails in my inbox. Links to books. Just sitting there. Waiting to be opened, read. For months already. The sender, you ask? Me. Paul van der Laken. A nuisance that guy, I tell you. He keeps sending me reminders, of stuff to do, books to read. Books he’s sure a more…

Awful AI: A curated list of scary usages of artificial intelligence

I found this amazingly horrifying list called Awful Artificial Intelligence: Artificial intelligence [AI] in its current state is unfair, easily susceptible to attacks and notoriously difficult to control. Nevertheless, more and more concerning the uses of AI technology are appearing in the wild. [Awful A.I.] aims to track all of them. We hope that Awful AI can be a platform to spur…

#100DaysOfCode: Machine Learning & Data Visualization

2018 seemed to be the year of challenges going viral on the web. Most of them were plain stupid and/or dangerous. However, one viral challenge I did like: #100DaysOfCode 1. Code minimum an hour every day for the next 100 days. 2. Tweet your progress every day with the #100DaysOfCode hashtag. 3. Each day, reach…

Papers with Code: State-of-the-Art

OK, this is a really great find! The website PapersWithCode.com lists all scientific publications of which the codes are open-sourced on GitHub. Moreover, you can sort these papers by the stars they accumulated on Github over the past days. The authors, @rbstojnic and @rosstaylor90, just made this in their spare time. Thank you, sirs! Papers with Code allows you to quickly…

Tensorflow for R Gallery

Tensorflow is a open-source machine learning (ML) framework. It’s primarily used to build neural networks, and thus very often used to conduct so-called deep learning through multi-layered neural nets.  Although there are other ML frameworks — such as Caffe or Torch — Tensorflow is particularly famous because it was developed by researchers of Google’s Brain…

Dragonflies and neural networks

Did you know that dragonflies are one of the most effective and accurate predators alive? And that while it has a brain consisting of very few neurons. Neuroscientist Greg Gage and his colleagues studied how a dragonfly locks onto its preys and captures it within milliseconds. Actually, a dragonfly seems to be little more than…

Computers decode what humans see: Generating images from brain activity

I recently got pointed towards a 2017 paper on bioRxiv that blew my mind: three researchers at the Computational Neuroscience Laboratories at Kyoto, Japan, demonstrate how they trained a deep neural network to decode human functional magnetic resonance imaging (fMRI) patterns and then generate the stimulus images. In simple words, the scholars used sophisticated machine learning to…