This is Noteaureus

Noteaureus.org is a place for notes about everything and anything that pops into jszym's head. At the moment, it's mostly a place to organise notes on deep learning papers, with a particular focus on graph learning.

The best way to browse the site is to take a look at the tags:

machine-learning (4) graph-learning (3) graphs (3) bioinformatics (2) spectral (2)

Notice any spelling mistakes or other inaccuracies? Shoot me a line at noteaureus [ΑΤ] jszym.com

Recent Notes

Functional Discovery via a Compendium of Expression Profiles
2021-02-02
Functional similarity based on co-expression
Papers function (gene)co-expression (gene)bioinformatics Timothy R. Hughes Matthew J. Marton Stephen H. Friend
Relational Inductive Biases, Deep Learning, and Graph Networks
2021-01-25
A pleasant review/positional paper that takes its time strolling through deep graph networks and how viewing them through the lens of relational inductive biases can be helpful.
Papers machine learning graphs graph learning inductive bias Peter W. Battaglia Razvan Pascanu
Position-Aware Graph Neural Networks
2021-01-08
A paper that addresses the problem of learning embeddings that encode for node position relative to the graph.
Papers machine learning graphs graph learning Jiaxuan You Rex Ying Jure Leskovec
Setting Up a Windows System to Protect Untechnical Users from Themselves
2020-12-27
Want to protect a friend or family from their untechnical selves? Here’s how I lock down systems for users with modest needs.
Sysadmin sysadmin windows
Multiple Sequence Aligning with STAR
2020-11-19
Do you have a bunch of reads from an RNA-Seq experiment? Because I do.
Bioinformatics bioinformatics sequence alignment
Semi-Supervised Classification with Graph Convolutional Networks
2020-10-06
Kipf, Thomas and M. Welling. “Semi-Supervised Classification with Graph Convolutional Networks.” ArXiv abs/1609.02907 (2017): n. pag.
Papers machine learning graphs graph learning spectral semi-supervised Thomas N. Kipf Max Welling
On Spectral Clustering: Analysis and Algorithm
2020-10-06
The paper which made spectral clustering practical. Useful for understanding other spectral techniques. Paper by M.I. Jordan & A. Ng., 2001
Papers machine learning clustering unsupervised spectral Michael Irwin Jordan Andrew Ng