Time Series Modeling with Hidden Variables and Gradient-Based Algorithms
暂无分享,去创建一个
[1] E. Lorenz. Deterministic nonperiodic flow , 1963 .
[2] P. Young,et al. Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.
[3] H. Akaike. INFORMATION THEORY AS AN EXTENSION OF THE MAXIMUM LIKELIHOOD , 1973 .
[4] E. Stear,et al. The simultaneous on-line estimation of parameters and states in linear systems , 1976 .
[5] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[6] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[7] Slava M. Katz,et al. Estimation of probabilities from sparse data for the language model component of a speech recognizer , 1987, IEEE Trans. Acoust. Speech Signal Process..
[8] Aravind K. Joshi,et al. An Introduction to Tree Adjoining Grammar , 1987 .
[9] Charles Herring,et al. Random number generators are chaotic , 1989, CACM.
[10] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..
[11] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[12] Richard Rohwer,et al. The "Moving Targets" Training Algorithm , 1989, NIPS.
[13] Martin Casdagli,et al. Nonlinear prediction of chaotic time series , 1989 .
[14] A. Krogh. A Cost Function for Internal Representations 733 A Cost Function for Internal Representations , 1989 .
[15] Geoffrey E. Hinton,et al. Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..
[16] Richard A. Harshman,et al. Indexing by Latent Semantic Analysis , 1990, J. Am. Soc. Inf. Sci..
[17] Esther Levin. Hidden control neural architecture modeling of nonlinear time varying systems and its applications , 1993, IEEE Trans. Neural Networks.
[18] Eric A. Wan,et al. Time series prediction by using a connectionist network with internal delay lines , 1993 .
[19] Lee A. Feldkamp,et al. Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks , 1994, IEEE Trans. Neural Networks.
[20] Jose C. Principe,et al. Reconstructed dynamics and chaotic signal modeling , 1994, Proceedings of IEEE Workshop on Neural Networks for Signal Processing.
[21] Yoshua Bengio,et al. An Input Output HMM Architecture , 1994, NIPS.
[22] Andreas S. Weigend,et al. Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .
[23] Yoshua Bengio,et al. Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.
[24] Carl E. Rasmussen,et al. In Advances in Neural Information Processing Systems , 2011 .
[25] Ronald J. Williams,et al. Gradient-based learning algorithms for recurrent networks and their computational complexity , 1995 .
[26] Geoffrey E. Hinton,et al. The "wake-sleep" algorithm for unsupervised neural networks. , 1995, Science.
[27] R. Durrett. Stochastic Calculus: A Practical Introduction , 1996 .
[28] Zoubin Ghahramani,et al. Learning Dynamic Bayesian Networks , 1997, Summer School on Neural Networks.
[29] David J. Field,et al. Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.
[30] Klaus-Robert Müller,et al. Analysis of Drifting Dynamics with Neural Network Hidden Markov Models , 1997, NIPS.
[31] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[32] Gunnar Rätsch,et al. Using support vector machines for time series prediction , 1999 .
[33] F. Girosi,et al. Nonlinear prediction of chaotic time series using support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.
[34] Srinivas Bangalore,et al. Complexity of lexical descriptions and its relevance to partial parsing , 1997 .
[35] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[36] Thorsten Joachims,et al. Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.
[37] Zoubin Ghahramani,et al. Learning Nonlinear Dynamical Systems Using an EM Algorithm , 1998, NIPS.
[38] Fiona Banner,et al. Further references , 1998, Afterall: A Journal of Art, Context and Enquiry.
[39] Michael Ruogu Zhang,et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.
[40] Geoffrey E. Hinton,et al. A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.
[41] S. Mallat. A wavelet tour of signal processing , 1998 .
[42] Vladimir Pavlovic,et al. Time-series classification using mixed-state dynamic Bayesian networks , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).
[43] Simon Haykin,et al. Support vector machines for dynamic reconstruction of a chaotic system , 1999 .
[44] Philip Resnik,et al. Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language , 1999, J. Artif. Intell. Res..
[45] P. Grassberger,et al. A robust method for detecting interdependences: application to intracranially recorded EEG , 1999, chao-dyn/9907013.
[46] Srinivas Bangalore,et al. Supertagging: An Approach to Almost Parsing , 1999, CL.
[47] F ChenStanley,et al. An Empirical Study of Smoothing Techniques for Language Modeling , 1996, ACL.
[48] D. Yang,et al. Drift Independent Volatility Estimation Based on High, Low, Open, and Close Prices , 2000 .
[49] C. Lee Giles,et al. Learning Chaotic Attractors by Neural Networks , 2000, Neural Computation.
[50] Yoshua Bengio,et al. A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..
[51] L. K. Hansen,et al. Independent Components in Text , 2000 .
[52] Rongchen Wang,et al. Genomic Analysis of a Nutrient Response in Arabidopsis Reveals Diverse Expression Patterns and Novel Metabolic and Potential Regulatory Genes Induced by Nitrate , 2000, Plant Cell.
[53] 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).
[54] Brendan J. Frey,et al. Factor graphs and the sum-product algorithm , 2001, IEEE Trans. Inf. Theory.
[55] J. Martinerie,et al. Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony , 2001, Journal of Neuroscience Methods.
[56] Gyözö Gidófalvi. Using News Articles to Predict Stock Price Movements , 2001 .
[57] T. Başar,et al. A New Approach to Linear Filtering and Prediction Problems , 2001 .
[58] Andrew McCallum,et al. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.
[59] Andreas Stolcke,et al. SRILM - an extensible language modeling toolkit , 2002, INTERSPEECH.
[60] David Barber,et al. Dynamic Bayesian Networks with Deterministic Latent Tables , 2002, NIPS.
[61] C. Rasmussen,et al. Gaussian Process Priors with Uncertain Inputs - Application to Multiple-Step Ahead Time Series Forecasting , 2002, NIPS.
[62] J. Martinerie,et al. Toward a Neurodynamical Understanding of Ictogenesis , 2003, Epilepsia.
[63] A. Schulze-Bonhage,et al. How well can epileptic seizures be predicted? An evaluation of a nonlinear method. , 2003, Brain : a journal of neurology.
[64] Léon Bottou,et al. Stochastic Learning , 2003, Advanced Lectures on Machine Learning.
[65] D. Pe’er,et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data , 2003, Nature Genetics.
[66] Holger Schwenk,et al. USING CONTINUOUS SPACE LANGUAGE MODELS FOR CONVERSATIONAL SPEECH RECOGNITION , 2003 .
[67] Neil D. Lawrence,et al. Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data , 2003, NIPS.
[68] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[69] Ilya Shmulevich,et al. On Learning Gene Regulatory Networks Under the Boolean Network Model , 2003, Machine Learning.
[70] Thomas L. Griffiths,et al. Integrating Topics and Syntax , 2004, NIPS.
[71] R. Tibshirani,et al. Least angle regression , 2004, math/0406456.
[72] John Blitzer,et al. Hierarchical Distributed Representations for Statistical Language Modeling , 2004, NIPS.
[73] Jouko Lampinen,et al. Time series prediction by Kalman smoother with cross-validated noise density , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).
[74] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[75] Erkki Oja,et al. Nonlinear dynamical factor analysis for state change detection , 2004, IEEE Transactions on Neural Networks.
[76] Zoubin Ghahramani,et al. Modeling T-cell activation using gene expression profiling and state-space models , 2004, Bioinform..
[77] A. Schulze-Bonhage,et al. Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic , 2004 .
[78] Mark Steyvers,et al. Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[79] Pierre Baldi,et al. On the relationship between deterministic and probabilistic directed Graphical models: From Bayesian networks to recursive neural networks , 2005, Neural Networks.
[80] Frederick Jelinek,et al. Some of my Best Friends are Linguists , 2005, Lang. Resour. Evaluation.
[81] Fabrizio Sebastiani,et al. An Analysis of the Relative Hardness of Reuters-21578 Subsets , 2003 .
[82] David J. Fleet,et al. Gaussian Process Dynamical Models , 2005, NIPS.
[83] Jürgen Schmidhuber,et al. Modeling systems with internal state using evolino , 2005, GECCO '05.
[84] Richard Bonneau,et al. The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo , 2006, Genome Biology.
[85] Zoubin Ghahramani,et al. A Bayesian approach to reconstructing genetic regulatory networks with hidden factors , 2005, Bioinform..
[86] Yoshua Bengio,et al. Hierarchical Probabilistic Neural Network Language Model , 2005, AISTATS.
[87] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[88] Dilek Z. Hakkani-Tür,et al. The AT&T WATSON speech recognizer , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..
[89] M. Barenco,et al. Ranked prediction of p53 targets using hidden variable dynamic modeling , 2006, Genome Biology.
[90] Andreas Schulze-Bonhage,et al. Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction. , 2006, Chaos.
[91] Guodong Liu,et al. Estimation of missing markers in human motion capture , 2006, The Visual Computer.
[92] Peter V. Gehler,et al. The rate adapting poisson model for information retrieval and object recognition , 2006, ICML.
[93] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[94] Mark Goadrich,et al. The relationship between Precision-Recall and ROC curves , 2006, ICML.
[95] Fu Jie Huang,et al. A Tutorial on Energy-Based Learning , 2006 .
[96] Kevin Murphy,et al. Modelling Gene Expression Data using Dynamic Bayesian Networks , 2006 .
[97] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[98] Trupti Joshi,et al. Inferring gene regulatory networks from multiple microarray datasets , 2006, Bioinform..
[99] Andrew McCallum,et al. Topics over time: a non-Markov continuous-time model of topical trends , 2006, KDD '06.
[100] John D. Lafferty,et al. Dynamic topic models , 2006, ICML.
[101] A. Schulze-Bonhage,et al. Do False Predictions of Seizures Depend on the State of Vigilance? A Report from Two Seizure‐Prediction Methods and Proposed Remedies , 2006, Epilepsia.
[102] Holger Schwenk,et al. Continuous Space Language Models for Statistical Machine Translation , 2006, ACL.
[103] A. Schulze-Bonhage,et al. Seizure anticipation by patients with focal and generalized epilepsy: A multicentre assessment of premonitory symptoms , 2006, Epilepsy Research.
[104] Yann LeCun,et al. Time-Delay Neural Networks and Independent Component Analysis for EEG-Based Prediction of Epileptic Seizures Propagation , 2007, AAAI.
[105] Erkki Oja,et al. Time series prediction competition: The CATS benchmark , 2007, Neurocomputing.
[106] Vladimir Pavlovic,et al. Conditional State Space Models for Discriminative Motion Estimation , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[107] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[108] Marc'Aurelio Ranzato,et al. Sparse Feature Learning for Deep Belief Networks , 2007, NIPS.
[109] Amy K. Schmid,et al. A Predictive Model for Transcriptional Control of Physiology in a Free Living Cell , 2007, Cell.
[110] Yann LeCun,et al. Discovering the hidden structure of house prices with a non-parametric latent manifold model , 2007, KDD '07.
[111] R. Yoshida,et al. Finding module-based gene networks with state-space models - Mining high-dimensional and short time-course gene expression data , 2007, IEEE Signal Processing Magazine.
[112] Shlomo Geva,et al. News Aware Volatility Forecasting: Is the Content of News Important? , 2007, AusDM.
[113] Geoffrey E. Hinton,et al. Three new graphical models for statistical language modelling , 2007, ICML '07.
[114] David M. Blei,et al. Supervised Topic Models , 2007, NIPS.
[115] Christopher S. Poultney,et al. Insights into the genomic nitrate response using genetics and the Sungear Software System. , 2007, Journal of experimental botany.
[116] Masao Nagasaki,et al. Recursive regularization for inferring gene networks from time-course gene expression profiles , 2009, BMC Systems Biology.
[117] Marc'Aurelio Ranzato,et al. Semi-supervised learning of compact document representations with deep networks , 2008, ICML '08.
[118] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[119] J. Cameron,et al. Real-Time Estimation of Missing Markers in Human Motion Capture , 2008, 2008 2nd International Conference on Bioinformatics and Biomedical Engineering.
[120] Vladimir Pavlovic,et al. 3D Human Motion Tracking Using Dynamic Probabilistic Latent Semantic Analysis , 2008, 2008 Canadian Conference on Computer and Robot Vision.
[121] Jason Weston,et al. A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.
[122] Yann LeCun,et al. Comparing SVM and convolutional networks for epileptic seizure prediction from intracranial EEG , 2008, 2008 IEEE Workshop on Machine Learning for Signal Processing.
[123] Geoffrey E. Hinton,et al. A Scalable Hierarchical Distributed Language Model , 2008, NIPS.
[124] Satoru Miyano,et al. Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models , 2008, Bioinform..
[125] Neil D. Lawrence,et al. Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities , 2008, ECCB.
[126] Geoffrey E. Hinton,et al. Improving a statistical language model through non-linear prediction , 2009, Neurocomputing.
[127] Yi Zhang,et al. An integrated machine learning approach for predicting DosR-regulated genes in Mycobacterium tuberculosis , 2009, BMC Systems Biology.
[128] R. Kuick,et al. Temporal quantitative proteomics by iTRAQ 2D-LC-MS/MS and corresponding mRNA expression analysis identify post-transcriptional modulation of actin-cytoskeleton regulators during TGF-beta-Induced epithelial-mesenchymal transition. , 2009, Journal of proteome research.
[129] Marc'Aurelio Ranzato,et al. Unsupervised Learning of Feature Hierarchies , 2009 .
[130] Yann LeCun,et al. Dynamic Factor Graphs for Time Series Modeling , 2009, ECML/PKDD.
[131] Wray L. Buntine. Estimating Likelihoods for Topic Models , 2009, ACML.
[132] Neil D. Lawrence,et al. Latent Force Models , 2009, AISTATS.
[133] Naoki Abe,et al. Grouped graphical Granger modeling for gene expression regulatory networks discovery , 2009, Bioinform..
[134] Isabel M. Tienda-Luna,et al. Reverse engineering gene regulatory networks , 2009, IEEE Signal Processing Magazine.
[135] Geoffrey E. Hinton,et al. Semantic hashing , 2009, Int. J. Approx. Reason..
[136] Yann LeCun,et al. Classification of patterns of EEG synchronization for seizure prediction , 2009, Clinical Neurophysiology.
[137] G. Krouk,et al. Nitrate signaling: adaptation to fluctuating environments. , 2010, Current opinion in plant biology.
[138] Srinivas Bangalore,et al. Feature-rich continuous language models for speech recognition , 2010, 2010 IEEE Spoken Language Technology Workshop.
[139] Charles Elkan,et al. Expectation Maximization Algorithm , 2010, Encyclopedia of Machine Learning.
[140] Satoru Miyano,et al. Network-Based Predictions and Simulations by Biological State Space Models: Search for Drug Mode of Action , 2010, Journal of Computer Science and Technology.
[141] Lawrence Carin,et al. Hierarchical Bayesian Modeling of Topics in Time-Stamped Documents , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[142] Marc'Aurelio Ranzato,et al. Dynamic auto-encoders for semantic indexing , 2010 .
[143] Klaus-Robert Müller,et al. Efficient BackProp , 2012, Neural Networks: Tricks of the Trade.