Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning

13 Mar 2018 Nicolas Papernot Patrick McDaniel

Deep neural networks (DNNs) enable innovative applications of machine learning like image recognition, machine translation, or malware detection. However, deep learning is often criticized for its lack of robustness in adversarial settings (e.g., vulnerability to adversarial inputs) and general inability to rationalize its predictions... (read more)

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