暂无分享,去创建一个
[1] A. Barron. Approximation and Estimation Bounds for Artificial Neural Networks , 1991, COLT '91.
[2] Kuldip K. Paliwal,et al. Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..
[3] Barbara Hammer,et al. On the approximation capability of recurrent neural networks , 2000, Neurocomputing.
[4] Hinrich Schütze,et al. Book Reviews: Foundations of Statistical Natural Language Processing , 1999, CL.
[5] Andrew R. Barron,et al. Approximation and estimation bounds for artificial neural networks , 2004, Machine Learning.
[6] Aapo Hyvärinen,et al. Estimation of Non-Normalized Statistical Models by Score Matching , 2005, J. Mach. Learn. Res..
[7] Leonhard Held,et al. Gaussian Markov Random Fields: Theory and Applications , 2005 .
[8] Douglas L. Brutlag,et al. Sequence Motifs: Highly Predictive Features of Protein Function , 2006, Feature Extraction.
[9] K. Ramanan,et al. Concentration Inequalities for Dependent Random Variables via the Martingale Method , 2006, math/0609835.
[10] Nando de Freitas,et al. On Autoencoders and Score Matching for Energy Based Models , 2011, ICML.
[11] P. Honeine,et al. Kernel-based autoregressive modeling with a pre-image technique , 2011, 2011 IEEE Statistical Signal Processing Workshop (SSP).
[12] Joel A. Tropp,et al. User-Friendly Tail Bounds for Sums of Random Matrices , 2010, Found. Comput. Math..
[13] Huan Wang,et al. Exact Recovery of Sparsely-Used Dictionaries , 2012, COLT.
[14] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[15] Anima Anandkumar,et al. Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank-1 Updates , 2014, ArXiv.
[16] Finale Doshi-Velez,et al. Hidden Parameter Markov Decision Processes: An Emerging Paradigm for Modeling Families of Related Tasks , 2014, AAAI Fall Symposia.
[17] Yoshua Bengio,et al. What regularized auto-encoders learn from the data-generating distribution , 2012, J. Mach. Learn. Res..
[18] Fei-Fei Li,et al. Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[19] Munther A. Dahleh,et al. Minimal realization problem for Hidden Markov Models , 2014, 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[20] Aryeh Kontorovich,et al. Uniform Chernoff and Dvoretzky-Kiefer-Wolfowitz-Type Inequalities for Markov Chains and Related Processes , 2012, J. Appl. Probab..
[21] Danqi Chen,et al. A Fast and Accurate Dependency Parser using Neural Networks , 2014, EMNLP.
[22] Anima Anandkumar,et al. Tensor decompositions for learning latent variable models , 2012, J. Mach. Learn. Res..
[23] Le Song,et al. Nonparametric Estimation of Multi-View Latent Variable Models , 2013, ICML.
[24] Anima Anandkumar,et al. A Spectral Algorithm for Latent Dirichlet Allocation , 2012, Algorithmica.
[25] Anima Anandkumar,et al. Score Function Features for Discriminative Learning: Matrix and Tensor Framework , 2014, ArXiv.
[26] Alexander J. Smola,et al. Fast and Guaranteed Tensor Decomposition via Sketching , 2015, NIPS.
[27] Anima Anandkumar,et al. Provable Methods for Training Neural Networks with Sparse Connectivity , 2014, ICLR.
[28] Anima Anandkumar,et al. Online tensor methods for learning latent variable models , 2013, J. Mach. Learn. Res..
[29] Lu Chen,et al. Recurrent Polynomial Network for Dialogue State Tracking with Mismatched Semantic Parsers , 2015, SIGDIAL Conference.
[30] Zachary Chase Lipton. A Critical Review of Recurrent Neural Networks for Sequence Learning , 2015, ArXiv.
[31] Anima Anandkumar,et al. Learning Overcomplete Latent Variable Models through Tensor Methods , 2014, COLT.
[32] Kamyar Azizzadenesheli,et al. Reinforcement Learning of POMDPs using Spectral Methods , 2016, COLT.
[33] Muhammad Ghifary,et al. Strongly-Typed Recurrent Neural Networks , 2016, ICML.
[34] Aapo Hyvärinen,et al. Density Estimation in Infinite Dimensional Exponential Families , 2013, J. Mach. Learn. Res..
[35] Anima Anandkumar,et al. Beating the Perils of Non-Convexity: Guaranteed Training of Neural Networks using Tensor Methods , 2017 .