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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FUSE: Fast Unified Simulation and Estimation for PDEs
Neural Information Processing Systems
Parametric PDEs consider different system conditions as well as an underlying solution operator characterized by a set of finite-dimensional parameters.
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Certifiably Adversarially Robust Detection of Out-of-Distribution Data
Neural Information Processing Systems
Deep neural networks are known to be overconfident when applied to out-of-distribution (OOD) inputs which clearly do not belong to any class.
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Attack of the Tails: Y es, You Really Can Backdoor Federated Learning
Neural Information Processing Systems
In this work, we provide evidence to the contrary.
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