The developers of the Automunge open source platform for tabular data preprocessing have taken a somewhat unorthodox approach to documentation and communications, making use of multimedia, blogging, tweets, jupyter notebooks, as well as music and photography in publication. This submission will offer an exhibited excerpt of such communication practices, featuring elements of multimedia videos with narration, accompanied with hand drawn slides and transcript, presented as both a brief introduction and extended walkthrough. We believe this form of presentation is a very accessible low cost option to communicate complex subject matter in a concise and accessible form. Further examples are also provided in the references.

Open Source Blogging with Automunge

Comic strips are a naturally appealing medium which provide a visually-attractive means for situating scientific results within a narrative.
Although they may not be relevant to all situations and can be time-consuming to produce, they also provide unique opportunities for humor and levity that may be an important tool in disseminating and convincing an audience of the merits of a paper.
Furthermore, their decomposition into panels makes it easy to annotate them using standard accessibility tools for images.
This paper presents the case for presenting scientific posters as comic strips, using the author's 2018 SacreBLEU poster as a motivating example.

Scientific dissemination via comic strip: A case study with SacreBLEU

Science is a debate.
Debates happen where there is wiggle room for interpretation. There is no debate when all parties agree, or when all parties know why they disagree.
Scientific debates can be settled by agreeing on an experimental protocol. Good protocols identify wiggle room and preemptively get rid of it, by fixing the interpretation of experimental results before the experiment proceeds. “Are doctors transmitting deadly illnesses from cadavers to birthing mothers? Have some doctors wash their hands after autopsies. Let us agree that if their patients have better survival rates than usual, it means that infections travel on the hands of doctors (Carter 1985).”
Experimental results might tell you which way the settlement goes, but ideally the debate itself ends with the protocol. From this point of view, Science is the art of defining convincing protocols: scientific papers are more interesting and more rigorous when they are written by two people who start out genuinely disagreeing.
Mimosa is an attempt at harnessing both support and disagreement in science into a productive, collaborative format. Mimosa also tries to address many of the numerous recognised issues within the current format for sharing science, born at a different time and for the wrong reasons.

When it first started, Wikipedia was greeted with suspicion. It is now a major platform for finding information, used by all demographics. Wikipedia has a famous rule: “No original research.”
Mimosa aspires to be that free, open-collaborative online platform created and maintained by a community of volunteer contributors, dedicated to original research.

The Mimosa Manifesto: a Web Platform for Open Collaboration in Science

We demonstrate the advantages of an interactive medium for explaining the key principles and mathematical machinery behind graph neural networks. We discuss the challenges we faced while creating an expository article on this topic using interactive elements. Our exhibit is available at https://distill.pub/2021/understanding-gnns/.

Interactive Media for Understanding ML Methods: A Case-Study on Graph Neural Networks

This exhibit demonstrates how boundary treatment in convolutional networks can incur foveation effects: Impacted pixels have fewer ways to contribute to the computation than central pixels. Different padding mechanisms can either eliminate or aggravate these effects, which is made obvious by an interactive visualization.

Convolution Can Incur Foveation Effects

I❤️LA is a compilable markdown for math. It can generate working code in C++ and Python (and more to come). The same I❤️LA code can also generate LaTeX which is in turn rendered as beautifully typeset math. The I❤️LA code creates a publishable artifact and reference implementations. We believe I❤️LA can improve expositional clarity, code reproducibility and interoperability, and scientific education. We focus our initial efforts on mathematical expressions found in the wild within the Computer Graphics community, but plan to extend our work to the larger Machine Learning and Computer Science community. In our proposed Rethinking ML Papers exhibit, we will demonstrate: results of our large-scale study and an interactive demo application.


I❤LA: Compilable Markdown for Linear Algebra

The recent explosion in machine learning research activity poses challenges for both researchers who aim to widely disseminate their work, as well as to readers who find it challenging to keep up with the onslaught of new research ideas. In this paper, we describe a workflow for creating a spectrum of machine learning research communication artifacts optimized to maximize the clarity of scientific communication, advance the fronts of explainability and interpretability, as well as empower the community to reproduce research software. The workflow describes creating a spectrum of communication artifacts including visuals, animations, interactive explorables, reproducible notebooks, open-source software, and software packages. The articles produced by this workflow have explained cutting-edge-ML research to a large audience and were read over three million times.

Machine learning research communication via illustrated and interactive web articles

Academic trade requires juggling multiple variants of the same content published in different formats: manuscripts, presentations, posters and computational notebooks. The need to track versions to accommodate for the write--review--rebut--revise life-cycle adds another layer of complexity. We propose to significantly reduce this burden by maintaining a single source document in a version-controlled environment (such as git), adding functionality to generate a collection of output formats popular in academia. To this end, we utilise various open-source tools from the Jupyter scientific computing ecosystem and operationalise selected software engineering concepts. We offer a proof-of-concept workflow that composes Jupyter Book (an online document), Jupyter Notebook (a computational narrative) and reveal.js slides from a single markdown source file. Hosted on GitHub, our approach supports change tracking and versioning, as well as a transparent review process based on the underlying code issue management infrastructure. An exhibit of our workflow can be previewed at https://so-cool.github.io/you-only-write-thrice/.

ICLR 2021 Workshop Rethinking ML Papers Exhibit and Workflow Submissions