From conferences to surveys
We reside in an interval of superlatives. Yearly, month, week, new developments in machine learning evaluation are launched. The number of (ML) papers added to arXiv is rising equally fast. Better than 11 000 papers have been added last October in the Computer Science Category.
Equally, large machine learning conferences are seeing ever-growing number of submissions — so many the reality is, that, to ensure a superb reviewing course of, submitting authors are required to perform reviewers for various submissions (known as reciprocal reviewing).
Each paper presumably introduces new evaluation outcomes, a model new approach, new datasets or benchmarks. As a beginner in Machine Learning, it’s troublesome to even get started: the amount of information is overwhelming. In a earlier article, I argued that and why ML beginners should read papers. The quintessence is that good evaluation papers are self-contained lectures that hone analytical pondering.
On this text, I give learners ideas on how and the place to go looking out attention-grabbing papers to study, a level that I didn’t completely elaborate beforehand. Over 7 steps, I info you via the doable technique of discovering and learning attention-grabbing papers.
Step 1: Resolve your topic of curiosity
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