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Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKA
Bayesian deep learning has always garnered substantial interest in the machine learning community.
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CORNN: Convex optimization of recurrent neural networks for rapid inference of neural dynamics Fatih Dinc Department of Applied Physics Stanford University Stanford, CA94305 Adam Shai
A promising way to extract computational principles from these large datasets is to train data-constrained recurrent neural networks (dRNNs).
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Weakly Coupled Deep Q-Networks
"subagents," one for each subproblem, and then combine their solutions to establish
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Neural Shuffle-Exchange Networks - Sequence Processing in O(n log n) Time
Karlis Freivalds, Emīls Ozoliņš, Agris Šostaks
Neural Information Processing Systems http://nips.cc/
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Neural Voice Cloning with a Few Samples
Sercan Arik, Jitong Chen, Kainan Peng, Wei Ping, Yanqi Zhou
Neural Information Processing Systems http://nips.cc/
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