Neural Nets

Poison Frogs! Targeted Poisoning Attacks on Neural Networks

in adversarial learning, machine learning

2018-04-11

What are poisoning attacks?

Before deep learning algorithms can be deployed in security-critical applications, their robustness against adversarial attacks must be put to the test. The existence of adversarial examples in deep neural networks (DNNs) has triggered debates on how secure these classifiers are. Adversarial examples fall within a category of attacks called evasion attacks. Evasion attacks happen at test time – a clean target instance is modified to avoid detection by a classifier, or spur misclassification.

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Training Quantized Nets: A Deeper Understanding

in machine learning, optimization

2017-11-10

Why quantized nets?

Deep neural networks are an integral part of state-of-the-art computer vision and natural language processing systems. Because of their high memory requirements and computational complexity, networks are usually trained using powerful hardware. There is an increasing interest in training and deploying neural networks directly on battery-powered devices, such as cell phones or other platforms. Such low-power embedded systems are memory and power limited, and in some cases lack basic support for floating-point arithmetic.

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