Prabhu, V.1, McAteer, M.2, Teehan, R.3
Rethinking ML Papers – ICLR 2021 Workshop
Papers are hard to write. Survey papers are just that much harder. From the authors’ perspective, challenges include the responsibility to not erase out important work being done by (sometimes) adversarially aligned research groups, finding the right semantic clustering to sub-categorize individual contributions, controlling for the verbosity and length of the final paper, ensuring an optimal mixing of personal opinion and the innate narratives in the paper(s) being cited, version controlling, ease of updating, and also the aesthetics of presentation. From the reader’s viewpoint, challenges include ease of reading, single-snapshot summarizability, portability, and being given the agency to edit or fork their own copies. Taking cues from the emergence of the cheat-sheet culture in machine learning and the virtues of living editable documentation and version control, we propose an interactive and live SVG format based methodology that we term SPICE: Survey Papers as Interactive Cheat-sheet Embedding. We cover the technical details behind constructing SPICEs and present an example gallery covering ‘hot button’ areas in machine learning such as Out of distribution detection, the ‘All you need’ histrionics and Transformer architectures. We have open-sourced all of the code with regards to this project here: https://anonymous.4open.science/r/7c5ee736-c876-4b90-97bd-49870eb6b63f/.
1 UnifyID
2 Deep Cell
3 Charles River Analytics
For More Information
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