Deep系の論文めも後半その１ (2016年夏頃までのもの対象)

後半その１...？そもそも後半でまとめるのに無理があった。

前半：「過学習の防止・学習のアルゴリズム」、「理論」、「画像・動画からのキャプション生成」

paper.hatenadiary.jp

生成モデル

Deep Convolutional Generative Adversarial Networks

Generative Adversarial Nets

Goodfellow, Ian, et al. "Generative adversarial nets." Advances in Neural Information Processing Systems. 2014.

papers.nips.cc

Adversarial Networks の画像生成に迫る @WBAFLカジュアルトーク#3 from Daiki Shimada

www.slideshare.net

Unsupervised representation learning with deep convolutional generative adversarial networks

Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015).

Deep Convolutional Generative Adversarial Networks - Nextremer勉強会資料 from tm_2648

www.slideshare.net

Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks

Denton, Emily L., Soumith Chintala, and Rob Fergus. "Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks." Advances in neural information processing systems. 2015.

Laplacian Pyramid of Generative Adversarial Networks (LAPGAN) - NIPS2015読み会 #nips_yomi from Koichi Hamada

www.slideshare.net

Energy-based Generative Adversarial Network

Zhao, Junbo, Michael Mathieu, and Yann LeCun. "Energy-based Generative Adversarial Network." arXiv preprint arXiv:1609.03126 (2016).

Pixel recurrent neural networks

pdf: [1601.06759] Pixel Recurrent Neural Networks

van den Oord, Aaron, Nal Kalchbrenner, and Koray Kavukcuoglu. "Pixel Recurrent Neural Networks." arXiv preprint arXiv:1601.06759 (2016).

論文紹介 Pixel Recurrent Neural Networks from Seiya Tokui

www.slideshare.net

WaveNet: A Generative Model for Raw Audio

deepmind.com

[DL輪読会]Wavenet a generative model for raw audio from DeepLearningJP2016

www.slideshare.net

その他資料

IIBMP2016 深層生成モデルによる表現学習 from Preferred Infrastructure & Preferred Networks

www.slideshare.net

モデルの圧縮・パラメータの削減

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 1MB model size

arxiv: [1602.07360] SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and &lt;0.5MB model size

Iandola, Forrest N., et al. "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 1MB model size." arXiv preprint arXiv:1602.07360 (2016).

qiita.com

"Techniques for Efficient Implementation of Deep Neural Networks," a Presentation from Stanford from Embedded Vision Alliance

www.slideshare.net

Eie: Efficient inference engine on compressed deep neural network

arxiv: [1602.01528] EIE: Efficient Inference Engine on Compressed Deep Neural Network

こちらにてスタンフォード大学の資料を直接閲覧できます。

その他

Learning to Compose Neural Networks for Question Answering

arxiv: [1601.01705] Learning to Compose Neural Networks for Question Answering

Andreas, Jacob, et al. "Learning to compose neural networks for question answering." arXiv preprint arXiv:1601.01705 (2016).

speakerdeck.com

Adaptive Computation Time for Recurrent Neural Networks

arxiv: [1603.08983] Adaptive Computation Time for Recurrent Neural Networks

Graves, Alex. "Adaptive Computation Time for Recurrent Neural Networks." arXiv preprint arXiv:1603.08983 (2016).

distill.pub