An Approximate Bayesian Long Short- Term Memory Algorithm for Outlier Detection
暂无分享,去创建一个
[1] Sharad Singhal,et al. Training Multilayer Perceptrons with the Extende Kalman Algorithm , 1988, NIPS.
[2] Geoffrey E. Hinton,et al. Keeping the neural networks simple by minimizing the description length of the weights , 1993, COLT '93.
[3] Fei-Fei Li,et al. Deep visual-semantic alignments for generating image descriptions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[4] Jeffrey Pennington,et al. GloVe: Global Vectors for Word Representation , 2014, EMNLP.
[5] Jürgen Schmidhuber,et al. Learning to forget: continual prediction with LSTM , 1999 .
[6] Nicolò Cesa-Bianchi,et al. Advances in Neural Information Processing Systems 31 , 2018, NIPS 2018.
[7] Hermann Hellwagner,et al. Social media for crisis management: clustering approaches for sub-event detection , 2015, Multimedia Tools and Applications.
[8] Julien Cornebise,et al. Weight Uncertainty in Neural Networks , 2015, ArXiv.
[9] Zoubin Ghahramani,et al. A Theoretically Grounded Application of Dropout in Recurrent Neural Networks , 2015, NIPS.
[10] Geoffrey E. Hinton,et al. Bayesian Learning for Neural Networks , 1995 .
[11] Georgiana Dinu,et al. Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors , 2014, ACL.
[12] Hermann Hellwagner,et al. Automatic sub-event detection in emergency management using social media , 2012, WWW.
[13] Rik Warren,et al. Use of Mahalanobis Distance for Detecting Outliers and Outlier Clusters in Markedly Non-Normal Data: A Vehicular Traffic Example , 2011 .
[14] Ron Meir,et al. Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights , 2014, NIPS.
[15] Rudolph van der Merwe,et al. The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).
[16] Ryan P. Adams,et al. Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks , 2015, ICML.
[17] Geir Evensen,et al. The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .
[18] Jeffrey K. Uhlmann,et al. Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.
[19] Alex Graves,et al. Practical Variational Inference for Neural Networks , 2011, NIPS.
[20] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[21] Julien Cornebise,et al. Weight Uncertainty in Neural Network , 2015, ICML.
[22] Zoubin Ghahramani,et al. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.
[23] Léon Personnaz,et al. A recursive algorithm based on the extended Kalman filter for the training of feedforward neural models , 1998, Neurocomputing.
[24] David J. C. MacKay,et al. A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.
[25] Yannis Stavrakas,et al. Degeneracy-Based Real-Time Sub-Event Detection in Twitter Stream , 2015, ICWSM.
[26] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[27] Alex Graves,et al. Generating Sequences With Recurrent Neural Networks , 2013, ArXiv.
[28] Andrew W. Senior,et al. Long short-term memory recurrent neural network architectures for large scale acoustic modeling , 2014, INTERSPEECH.