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• Drop-DTW: A Differentiable Method for Sequence Alignment that can Handle Outliers

  The problem of sequence-to-sequence alignment is central to many computational applications. Aligning two sequences (e.g., temporal signals) entails computing the optimal pairwise correspondence between the sequence elements while preserving their match orderings.

• FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout

  Despite the soaring use of convolutional neural networks (CNNs) in mobile applications, uniformly sustaining high-performance inference on mobile has been elusive due to the excessive computational demands of modern CNNs and the increasing diversity of deployed devices. A popular alternative comprises offloading CNN processing to powerful cloud-based servers. Nevertheless, by relying on the cloud to produce outputs, emerging mission-critical and high-mobility applications, such as drone obstacle avoidance or interactive applications, can suffer from the dynamic connectivity conditions and the uncertain availability of the cloud. In this paper we propose SPINN:

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