Beyond Static Papers: Rethinking How We Share Scientific Understanding in ML

Krishna Murthy Jatavallabhula · Bhairav Mehta · Tegan Maharaj · Amy Tabb · Khimya Khetarpal · Aditya Kusupati · Anna Rogers · Sara Hooker · Breandan Considine · Devi Parikh · Derek Nowrouzezahrai · Yoshua Bengio


Over the last decade, the volume of conference submissions in machine learning has broken records. Despite rapid advancements and increasing hype around AI, there is growing concern in the ML community about where the field is headed. The current pandemic gives researchers a long-awaited opportunity to pause and reflect: what kind of legacy do we want to leave behind? How are scientific results presented? How do we interpret and explain them? Does this process include and/or allow access to all stakeholders? Are the results reproducible? These are some of the many facets of effective scientific communication which will shape the next decade of ML research.

How much research is overlooked due to inaccessible communication? How many papers will be as readable in ten or twenty years? How can we make the proceedings more accessible for future generations of ML researchers? These are a few of the questions we plan to discuss in our workshop. We hope to instigate an exciting discussion on redesigning the scientific paper for the next few years of machine learning research!

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Timezone: America/Los_Angeles »