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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Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces
Neural Information Processing Systems
In the recent years, the attention of the community shifted towards models with structured latent variables.
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Dynamic Inference with Neural Interpreters
Neural Information Processing Systems
Modern neural network architectures can leverage large amounts of data to generalize well within the training distribution.
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