ICML-2015 Paper Digest

Every paper, ordered by impact or potential impact. About ICML 2015

IDPaperAuthors
1Batch Normalization: Accelerating Deep Network Training By Reducing Internal Covariate Shift
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Highlight: We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs.		Sergey Ioffe; Christian Szegedy;
2Show, Attend And Tell: Neural Image Caption Generation With Visual Attention
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Highlight: Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images.		Kelvin Xu; Jimmy Ba; Ryan Kiros et al.
3Unsupervised Domain Adaptation By Backpropagation
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Highlight: Here, we propose a new approach to domain adaptation in deep architectures that can be trained on large amount of labeled data from the source domain and large amount of unlabeled data from the target domain (no labeled target-domain data is necessary).		Yaroslav Ganin; Victor Lempitsky;
4DRAW: A Recurrent Neural Network For Image Generation
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Highlight: This paper introduces the Deep Recurrent Attentive Writer (DRAW) architecture for image generation with neural networks.		Karol Gregor; Ivo Danihelka; Alex Graves et al.
5Deep Learning With Limited Numerical Precision
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Highlight: We study the effect of limited precision data representation and computation on neural network training.		Suyog Gupta; Ankur Agrawal; Kailash Gopalakrishnan et al.

About ICML 2015

This page lists the papers of International Conference on Machine Learning (ICML 2015), ordered by impact or potential impact.

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