Neural Nets

Poison Frogs! Targeted Poisoning Attacks on Neural Networks

Data poisoning is an adversarial attack in which examples are added to the training set of a classifier to manipulate the behavior of the model at test time. We propose a new poisoning attack that is effective on neural nets, and can be executed by an outsider with no control over the training process.

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Training Quantized Nets: A Deeper Understanding

Neural net parameters can often be compressed down to just one single bit without a significant loss in network performance, yielding a huge reduction in memory footprint and computational workload. We develop a theory of quantized nets, and explain the performance of algorithms for weight quantization.

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