NIPS is a single-track machine learning and computational neuroscience conference that includes invited talks, demonstrations and oral and poster presentations of refereed papers.

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A simple neural network module for relational reasoning
Adam Santoro and David Raposo and David G. T. Barrett and Mateusz Malinowski and Razvan Pascanu and Peter Battaglia and Timothy Lillicrap
arXiv e-Print archive - 2017 via Local arXiv
Keywords: cs.CL, cs.LG

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Bayesian Compression for Deep Learning
Christos Louizos and Karen Ullrich and Max Welling
arXiv e-Print archive - 2017 via Local arXiv
Keywords: stat.ML, cs.LG

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Summary from gngdb
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Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models
Sergey Ioffe
arXiv e-Print archive - 2017 via Local arXiv
Keywords: cs.LG

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Summary from Qure.ai
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Summary from Sina Honari
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Triangle Generative Adversarial Networks
Zhe Gan and Liqun Chen and Weiyao Wang and Yunchen Pu and Yizhe Zhang and Hao Liu and Chunyuan Li and Lawrence Carin
arXiv e-Print archive - 2017 via Local arXiv
Keywords: cs.LG, stat.ML

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Summary from Sina Honari
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Self-Normalizing Neural Networks
Günter Klambauer and Thomas Unterthiner and Andreas Mayr and Sepp Hochreiter
arXiv e-Print archive - 2017 via Local arXiv
Keywords: cs.LG, stat.ML

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Summary from Joseph Paul Cohen
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Summary from Joseph Paul Cohen
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