Am Neumarkt 😱

#ML Geometric Deep Learning is an attempt to unify deep learning using geometry. Instead of building deep neural networks ignoring the symmetries in the data and leaving it to be discovered by the network, we apply the symmetries in the problem to the network. For example, instead of flattening the matrix of a cat image and have some predetermined order of the pixels, we apply a translational transformation on the 2D image and the cat should also be a cat without any doubt. This transformation can be enforced in the network. BTW, If you come from a physics background, it is most likely that you have heard about the symmetries in physical theories like Noether's theorem. In the history of physics, there was an era of many theories yet most of them are connected or even unified under the umbrella of geometry. Geometric deep learning is another "benevolent propaganda" based on a similar idea. References: 1. Bronstein, Michael. “ICLR 2021 Keynote - ‘Geometric Deep Learning: The Erlangen Programme of ML’ - M Bronstein.” Video. YouTube, June 8, 2021. https://www.youtube.com/watch?v=w6Pw4MOzMuo. 2. Bronstein MM, Bruna J, LeCun Y, Szlam A, Vandergheynst P. Geometric deep learning: going beyond Euclidean data. arXiv [cs.CV]. 2016. Available: http://arxiv.org/abs/1611.08097 3. Bronstein MM, Bruna J, Cohen T, Veličković P. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. arXiv [cs.LG]. 2021. Available: http://arxiv.org/abs/2104.13478

#DS A library for interactive visualization directly from pandas. https://github.com/santosjorge/cufflinks

#ML The Bayesian hierarchical model provides a process to use Bayesian inference hierarchically to update the posteriors. What is a Bayesian model? In a Bayesian linear regression problem, we can take the posterior from the previous data points and use it as our new prior for inferring based on new data. In other words, as more data coming in, our belief is being updated. However, this is a problem if some clusters in the dataset have small sample sizes, aka small support. As we take these samples and fit them onto the model, we may get a huge credible interval. One simple idea to mitigate this problem is to introduce some constraints on how the priors can change. For example, we can introduce a hyperprior that is parametrized by new parameters. Then the model becomes hierarchical since we will also have to model the new parameters. The referenced post, "Bayesian Hierarchical Modeling at Scale", provides some examples of coding such models using numpyro with performance in mind. https://florianwilhelm.info/2020/10/bayesian_hierarchical_modelling_at_scale/

#academia https://www.nature.com/articles/d41586-021-01468-z

#fun Germany, birthplace of the automobile, just gave the green light to robotaxis https://fortune-com.cdn.ampproject.org/c/s/fortune.com/2021/05/28/germany-automobile-legalize-robotaxi-autonomous-vehicle/amp/

#DS This paper serves as a good introduction to the declarative data analytics tools. Declarative analytics performs data analysis using a declarative syntax instead of functions for specific algorithms. Using declarative syntax, one can “describe what you want the program to achieve rather than how to achieve it”. To be declarative, the declarative language has to be specific on the tasks. With this, we can only turn the knobs of some predefined model. To me, this is a deal-breaker. Anyways, this paper is still a good read. Makrynioti N, Vassalos V. Declarative Data Analytics: A Survey. IEEE Trans Knowl Data Eng. 2021;33: 2392–2411. doi:10.1109/TKDE.2019.2958084 http://dx.doi.org/10.1109/TKDE.2019.2958084

#DS https://octo.github.com/projects/flat-data Hmmm, so they gave it a name. I've built so many projects using this approach. I started building such data repos using CI/CD services way before github actions was born. Of course github actions made it much easier. One of them is the EU covid data tracking project ( https://github.com/covid19-eu-zh/covid19-eu-data ). It's been running for more than a year with very little maintenance. Some covid projects even copied our EU covid data tracking setup. I actually built a system (https://dataherb.github.io) to pull such github actions based data scraping repos together.

#ML An interesting talk: ------------------- Dear all, We are pleased to have Anna Golubeva speak on "Are wider nets better given the same number of parameters?" on Wednesday May 19th at 12:00 ET. You can find further details here and listen to the talk here. We hope you can join! Best, Sven

#DS “Don’t pull down the data. Do it with SQL.” https://hakibenita.com/sql-for-data-analysis

#career#DS I believe this article is relevant. Most data scientists have very good academic records. These experiences of excellence compete with another required quality in the industry: The ability to survive in a less ideal yet competitive environment. We could be stubborn and find the environment that we fit well in or adapt based on the business playbook. Either way is good for us as long as we find the path that we love. (I have a joke about this article: To reasoning productively, we do not need references for our claims at all.) https://hbr.org/1991/05/teaching-smart-people-how-to-learn#

#DS#EDA#Visualization If you are keen on data visualization, the new Observable Plot is something exciting for you. Observable Plot is based on d3 but it is easier to use in Observable Notebook. It also follows the guidelines of the layered grammar of graphics (e.g., marks, scales, transforms, facets.). https://observablehq.com/@observablehq/plot

#DS (This is an automated post by IFTTT.) It is always good for a data scientist to understand more about data engineering. With some basic data engineering knowledge in mind, we can navigate through the blueprint of a fully productionized data project at any time. In this blog post, I listed some of the key concepts and tools that I learned in the past. This is my blog post on Datumorphism https://datumorphism.leima.is/wiki/data-engeering-for-data-scientist/checklist/