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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Transformer in Transformer Kai Han 1,2 An Xiao 2 Enhua Wu
Neural Information Processing Systems
Features of both words and sentences will be aggregated to enhance the representation ability.
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9f1d5659d5880fb427f6e04ae500fc25-Paper.pdf
Neural Information Processing Systems
We use the discrete variablezd,t to represent the topic that thetth word is generated from.
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Balanced Training for Sparse GANs Yite Wang
Neural Information Processing Systems
Despite its potential benefits, applying DST to GANs presents challenges due to the adversarial nature of the training process.
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7608de7a475c0c878f60960d72a92654-Paper.pdf
Neural Information Processing Systems
Introspection reveals that our meta learned LAs learn through fast association in a way that is qualitatively different from gradientdescent.
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