IEEE

Data ID Extraction Networks for Unsupervised Class- and Classifier-Free Detection of Adversarial Examples

Abstract: Deep neural networks (DNNs) have achieved satisfactory performance in multiple fields. However, recent studies have shown that DNNs can be easily fooled by adversarial examples. To mitigate ...
InfoQ

Evaluating AI Agents in Practice: Benchmarks, Frameworks, and Lessons Learned

This article introduces practical methods for evaluating AI agents operating in real-world environments. It explains how to ...
IEEE

Optimizing Neural Network Architecture for Binary Classification Using Evolutionary Algorithms

Abstract: Neural network performance heavily depends on the architecture chosen for specific tasks, such as binary classification. Rather than relying on conventional ...