Recent research has uncovered significant security vulnerabilities in open-source machine learning (ML) frameworks, putting sensitive data and operations at risk. As ML adoption grows across industries, so does the urgency of addressing these threats. The vulnerabilities, identified in a report by JFrog, reveal gaps in ML security compared to more established systems like DevOps and
How (and Where) ML Beginners Can Find Papers | by Pascal Janetzky | Dec, 2024
From conferences to surveys
We are living in an era of superlatives. Each year, month, week, new advancements in machine learning research are announced. The number of (ML) papers added to arXiv is growing equally fast. More than 11 000 papers have been added last October in the Computer Science Category.
Similarly, large machine learning conferences are seeing ever-growing number of submissions — so many in fact, that, to ensure a fair reviewing process, submitting authors are required to serve as reviewers for other submissions (called reciprocal reviewing).
Each paper possibly introduces new research results, a new method, new datasets or benchmarks. As a beginner in Machine Learning, it’s difficult to even get started: the amount of information is overwhelming. In a previous article, I argued that and why ML beginners should read papers. The quintessence is that good research papers are self-contained lectures that hone analytical thinking.
In this article, I give beginners ideas on how and where to find interesting papers to read, a point that I did not fully elaborate previously. Over 7 steps, I guide you through the possible process of finding and reading interesting papers.
Step 1: Determine your field of interest
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