Machine Learning for Offensive Security
Machine learning is revolutionizing offensive security, giving hackers powerful tools to detect vulnerabilities. With algorithms that can rapidly identify weaknesses, cyber attackers are more efficient than ever. How can we protect against this evolving threat?
Some applied research problems in machine learning
Machine learning has transformed industries, but researchers still face challenges such as bias in algorithms, interpretability of models, and scalability of data. These applied problems are crucial for the advancement of the field.
Arithmetic Formats for Machine Learning – Report from IEEE WG P3109 – ARITH 2024 [pdf]
Check out the latest advancements in arithmetic formats for machine learning as discussed in the IEEE WG P3109 at ARITH 2024. Download the full report [pdf] to stay informed!
IEEE FP8 Formats for Machine Learning (Draft) [pdf]
The IEEE FP8 Formats for Machine Learning draft proposes standardized formats for floating-point numbers in machine learning operations. This could lead to better interoperability between different systems and improved accuracy in calculations.
Ask HN: Machine learning engineers, what do you do at work?
Step into the world of machine learning engineers as they share insights into their day-to-day activities. From training models to optimizing algorithms, discover the fascinating work these professionals do to bring AI to life.
