暂无分享,去创建一个
Dustin Tran | Roger B. Grosse | Paul Vicol | Jimmy Ba | Yeming Wen | Jimmy Ba | Yeming Wen | Dustin Tran | Paul Vicol | R. Grosse
[1] Wojciech Zaremba,et al. Recurrent Neural Network Regularization , 2014, ArXiv.
[2] Sean Gerrish,et al. Black Box Variational Inference , 2013, AISTATS.
[3] Alex Graves,et al. Practical Variational Inference for Neural Networks , 2011, NIPS.
[4] Beatrice Santorini,et al. Building a Large Annotated Corpus of English: The Penn Treebank , 1993, CL.
[5] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[6] Alexander D'Amour,et al. Reducing Reparameterization Gradient Variance , 2017, NIPS.
[7] Sanjiv Kumar,et al. On the Convergence of Adam and Beyond , 2018 .
[8] Shane Legg,et al. Noisy Networks for Exploration , 2017, ICLR.
[9] Ingo Rechenberg,et al. Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .
[10] Marcin Andrychowicz,et al. Parameter Space Noise for Exploration , 2017, ICLR.
[11] Richard Socher,et al. Regularizing and Optimizing LSTM Language Models , 2017, ICLR.
[12] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[13] Jürgen Schmidhuber,et al. Training Recurrent Networks by Evolino , 2007, Neural Computation.
[14] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[15] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[16] Aaron C. Courville,et al. Recurrent Batch Normalization , 2016, ICLR.
[17] Geoffrey E. Hinton,et al. Keeping the neural networks simple by minimizing the description length of the weights , 1993, COLT '93.
[18] Yann LeCun,et al. Regularization of Neural Networks using DropConnect , 2013, ICML.
[19] R. J. Williams,et al. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.
[20] Erhardt Barth,et al. Recurrent Dropout without Memory Loss , 2016, COLING.
[21] Yoshua Bengio,et al. Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations , 2016, ICLR.
[22] Xi Chen,et al. Evolution Strategies as a Scalable Alternative to Reinforcement Learning , 2017, ArXiv.
[23] Julien Cornebise,et al. Weight Uncertainty in Neural Networks , 2015, ArXiv.
[24] David A. Patterson,et al. In-datacenter performance analysis of a tensor processing unit , 2017, 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA).
[25] Alexander J. Smola,et al. Fastfood: Approximate Kernel Expansions in Loglinear Time , 2014, ArXiv.
[26] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[27] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[28] Ariel D. Procaccia,et al. Variational Dropout and the Local Reparameterization Trick , 2015, NIPS.
[29] Max Welling,et al. Bayesian Compression for Deep Learning , 2017, NIPS.
[30] Zoubin Ghahramani,et al. A Theoretically Grounded Application of Dropout in Recurrent Neural Networks , 2015, NIPS.
[31] Karol Gregor,et al. Neural Variational Inference and Learning in Belief Networks , 2014, ICML.