Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Yesterday we showed you that we embrace the #future #mobility. Today I would like to invite you to a free training on Operide, our micro-mobility fleet management application based on #ai! So you can make sure that you provide a more balanced distribution of #eBikes or even #eScooters! Operide, our #ai driven shared micro-mobility fleet management application, optimises the rebalancing process so that more assets (bikes/scooters) are available to the end-user.

Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks. Unfortunately, the high cost of generating strong adversarial examples makes standard adversarial training impractical on large-scale problems like ImageNet. We present an algorithm that eliminates the overhead cost of generating adversarial examples by recycling the gradient information computed when updating model parameters. Our "free" adversarial training algorithm achieves state-of-the-art robustness on CIFAR-10 and CIFAR-100 datasets at negligible additional cost compared to natural training, and can be 7 to 30 times faster than other strong adversarial training methods. Using a single workstation with 4 P100 GPUs and 2 days of runtime, we can train a robust model for the large-scale ImageNet classification task that maintains 40% accuracy against PGD attacks.