Structured Inference Networks for Nonlinear State Space Models
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[1] Illtyd Trethowan. Causality , 1938 .
[2] Geoffrey E. Hinton,et al. The "wake-sleep" algorithm for unsupervised neural networks. , 1995, Science.
[3] Eric A. Wan,et al. Dual Kalman Filtering Methods for Nonlinear Prediction, Smoothing and Estimation , 1996, NIPS.
[4] Zoubin Ghahramani,et al. Learning Nonlinear Dynamical Systems Using an EM Algorithm , 1998, NIPS.
[5] Volker Tresp,et al. Fisher Scoring and a Mixture of Modes Approach for Approximate Inference and Learning in Nonlinear State Space Models , 1998, NIPS.
[6] 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).
[7] Juha Karhunen,et al. An Unsupervised Ensemble Learning Method for Nonlinear Dynamic State-Space Models , 2002, Neural Computation.
[8] Juha Karhunen,et al. State Inference in Variational Bayesian Nonlinear State-Space Models , 2006, ICA.
[9] Tapani Raiko,et al. Variational Bayesian learning of nonlinear hidden state-space models for model predictive control , 2009, Neurocomputing.
[10] Hugo Larochelle,et al. The Neural Autoregressive Distribution Estimator , 2011, AISTATS.
[11] Thomas B. Schön,et al. System identification of nonlinear state-space models , 2011, Autom..
[12] Yoshua Bengio,et al. Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription , 2012, ICML.
[13] Karol Gregor,et al. Neural Variational Inference and Learning in Belief Networks , 2014, ICML.
[14] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[15] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[16] Yoshua Bengio,et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.
[17] Christian Osendorfer,et al. Learning Stochastic Recurrent Networks , 2014, NIPS 2014.
[18] Zhe Gan,et al. Deep Temporal Sigmoid Belief Networks for Sequence Modeling , 2015, NIPS.
[19] Yoshua Bengio,et al. A Recurrent Latent Variable Model for Sequential Data , 2015, NIPS.
[20] Shakir Mohamed,et al. Variational Inference with Normalizing Flows , 2015, ICML.
[21] Richard E. Turner,et al. Neural Adaptive Sequential Monte Carlo , 2015, NIPS.
[22] Uri Shalit,et al. Deep Kalman Filters , 2015, ArXiv.
[23] Alex Graves,et al. DRAW: A Recurrent Neural Network For Image Generation , 2015, ICML.
[24] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[25] Martin A. Riedmiller,et al. Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images , 2015, NIPS.
[26] Diederik P. Kingma,et al. Variational Recurrent Auto-Encoders , 2014, ICLR.
[27] Ole Winther,et al. How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks , 2016, ICML 2016.
[28] John Salvatier,et al. Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.
[29] Ryan P. Adams,et al. Structured VAEs: Composing Probabilistic Graphical Models and Variational Autoencoders , 2016 .
[30] Ryan P. Adams,et al. Composing graphical models with neural networks for structured representations and fast inference , 2016, NIPS.
[31] Il Memming Park,et al. BLACK BOX VARIATIONAL INFERENCE FOR STATE SPACE MODELS , 2015, 1511.07367.
[32] Dustin Tran,et al. Variational Gaussian Process , 2015, ICLR.
[33] Ole Winther,et al. Sequential Neural Models with Stochastic Layers , 2016, NIPS.
[34] John P. Cunningham,et al. Linear dynamical neural population models through nonlinear embeddings , 2016, NIPS.