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Found 2 papers, 1 papers with code
Learning from What We Know: How to Perform Vulnerability Prediction using Noisy Historical Data
To tackle these issues, we propose TROVON, a technique that learns from known vulnerable components rather than from vulnerable and non-vulnerable components, as typically performed.
Machine Translation
Cryptography and Security
Software Engineering
Scalable Object Detection for Stylized Objects
Following recent breakthroughs in convolutional neural networks and monolithic model architectures, state-of-the-art object detection models can reliably and accurately scale into the realm of up to thousands of classes.
