Deep Learning
New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
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Resource-Adaptive Federated Learning with All-In-One Neural Composition
Neural Information Processing Systems
Conventional Federated Learning (FL) systems inherently assume a uniform processing capacity among clients for deployed models. However, diverse client hardware often leads to varying computation resources in practice.
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1b4839ff1f843b6be059bd0e8437e975-Paper-Conference.pdf
Neural Information Processing Systems
We introduce the Globally Normalized Autoregressive Transducer (GNAT) for addressing thelabel biasproblem instreaming speech recognition. Oursolution admits a tractable exact computation of the denominator for the sequence-level normalization.
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