Neural events compress event camera streams into fewer informative tokens via discrete asynchronous autoencoders, achieving on-par or better performance on detection and classification with 2x lower event rate.
In: IEEE Conf
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A tensor-based batch fuzzing framework with adaptive perturbation scaling from specification ranges achieves up to 40X higher throughput and 4X more detected violations than sequential baselines on DNN benchmarks.
Industry AI practitioners view model quality through nine attributes with context-dependent priorities, where data imbalance is a key challenge addressed by strategies like active learning, as confirmed by interviews and a follow-up survey.
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