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InfoGAN: Interpretable Representation Learning by ...
representation learning [1,2], whose goal is to use unlabelled data to learn a representation that exposes important semantic features as easily decodable factors. A method that can learn such representations is likely to exist [2], and to be useful for …
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Deep Residual Learning for Image Recognition - …
arxiv.orgDeep Residual Learning for Image Recognition Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research fkahe, v-xiangz, v-shren, jiansung@microsoft.com
Mastering Chess and Shogi by Self-Play with a …
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arXiv:0706.3639v1 [cs.AI] 25 Jun 2007
arxiv.orgarXiv:0706.3639v1 [cs.AI] 25 Jun 2007 Technical Report IDSIA-07-07 A Collection of Definitions of Intelligence Shane Legg IDSIA, Galleria …
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arXiv:1301.3781v3 [cs.CL] 7 Sep 2013
arxiv.orgFor all the following models, the training complexity is proportional to O = E T Q; (1) where E is number of the training epochs, T is the number of …
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@google.com arXiv:1609.03499v2 [cs.SD] 19 Sep 2016
arxiv.orgwhere 1 <x t <1 and = 255. This non-linear quantization produces a significantly better reconstruction than a simple linear quantization scheme. …
A Tutorial on UAVs for Wireless Networks: …
arxiv.orgA Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems Mohammad Mozaffari 1, ... to UAVs in wireless communications is the work in …
Adversarial Generative Nets: Neural Network …
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Massive Exploration of Neural Machine Translation ...
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