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A comprehensive artificial intelligence system needs to not only perceive the environment with different `senses' (e.g., seeing and hearing) but also infer the world's conditional (or even causal) relations and corresponding uncertainty.
Receptive fields and functional architecture of monkey striate cortex
David H Hubel and Torsten N Wiesel · 1968
Earlier work this paper cites.
Modeles connexionnistes de l’apprentissage (connectionist learning models)
Y. LeCun · 1987
Earlier work this paper cites.
Auto-association by multilayer perceptrons and singular value decomposition
Hervé Bourlard and Yves Kamp · 1988
Earlier work this paper cites.
Forecasting, structural time series models and the Kalman filter
Andrew C Harvey · 1990
Earlier work this paper cites.
A practical Bayesian framework for backprop networks
JC MacKay David · 1992
Earlier work this paper cites.
Connectionist learning of belief networks
Radford M. Neal · 1992
Earlier work this paper cites.
Keeping the neural networks simple by minimizing the description length of the weights
Geoffrey E Hinton and Drew Van Camp · 1993
Earlier work this paper cites.
Autoencoders, minimum description length, and Helmholtz free energy
Geoffrey E Hinton and Richard S Zemel · 1994
Earlier work this paper cites.
An introduction to the conjugate gradient method without the agonizing pain
Jonathan R Shewchuk · 1994
Earlier work this paper cites.
Bayesian learning for neural networks
Radford M Neal · 1995
Earlier work this paper cites.
An introduction to Bayesian networks
Finn V Jensen et al · 1996
Earlier work this paper cites.
Stochastic processes
Sheldon M Ross, John J Kelly, Roger J Sullivan, William James Perry, Donald Mercer, Ruth M Davis, Thomas Dell Washburn, Earl V Sager, Joseph B Boyce, and Vincent L Bristow · 1996
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Bayesian Forecasting & Dynamic Models
Jeff Harrison and Mike West · 1999
Earlier work this paper cites.
An introduction to variational methods for graphical models
Michael I. Jordan, Zoubin Ghahramani, Tommi Jaakkola, and Lawrence K. Saul · 1999
Earlier work this paper cites.
Matrix Variate Distributions
A.K. Gupta and D.K. Nagar · 2000
Earlier work this paper cites.
Estimation and prediction for stochastic blockstructures
Krzysztof Nowicki and Tom A B Snijders · 2001
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
Geoffrey E. Hinton · 2002
Earlier work this paper cites.
Latent Dirichlet allocation
David M Blei, Andrew Y Ng, and Michael I Jordan · 2003
Earlier work this paper cites.
A Guide to Distribution Theory and Fourier Transforms
Robert S Strichartz · 2003
Earlier work this paper cites.
Learning users’ interests by quality classification in market-based recommender systems
Yan Zheng Wei, Luc Moreau, and Nicholas R. Jennings · 2005
Earlier work this paper cites.
Pattern Recognition and Machine Learning
Christopher M. Bishop · 2006
Earlier work this paper cites.
Correlated topic models
David Blei and John Lafferty · 2006
Earlier work this paper cites.
Dynamic topic models
David M Blei and John D Lafferty · 2006
Earlier work this paper cites.
Product of Gaussians for speech recognition
M. J. F. Gales and S. S. Airey · 2006
Earlier work this paper cites.
A fast learning algorithm for deep belief nets
Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh · 2006
Earlier work this paper cites.
Efficient learning of sparse representations with an energy-based model
Christopher Poultney, Sumit Chopra, Yann L Cun, et al · 2006
Earlier work this paper cites.
Probabilistic matrix factorization
Ruslan Salakhutdinov and Andriy Mnih · 2007
Earlier work this paper cites.
Restricted Boltzmann machines for collaborative filtering
Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey E. Hinton · 2007
Earlier work this paper cites.
Discovering and exploiting causal dependencies for robust mobile context-aware recommenders
Ghim-Eng Yap, Ah-Hwee Tan, and HweeHwa Pang · 2007
Earlier work this paper cites.
Collaborative filtering for implicit feedback datasets
Yifan Hu, Yehuda Koren, and Chris Volinsky · 2008
Earlier work this paper cites.
Fast collapsed gibbs sampling for latent Dirichlet allocation
Ian Porteous, David Newman, Alexander Ihler, Arthur Asuncion, Padhraic Smyth, and Max Welling · 2008
Earlier work this paper cites.
Bayesian probabilistic matrix factorization using markov chain monte carlo
Ruslan Salakhutdinov and Andriy Mnih · 2008
Earlier work this paper cites.
Continuous time dynamic topic models
Chong Wang, David M. Blei, and David Heckerman · 2008
Earlier work this paper cites.
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y Ng · 2009
Earlier work this paper cites.
Relation regularized matrix factorization
Wu-Jun Li and Dit-Yan Yeung · 2009
Earlier work this paper cites.
Online learning for latent Dirichlet allocation
Matthew Hoffman, Francis R Bach, and David M Blei · 2010
Earlier work this paper cites.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol · 2010
Earlier work this paper cites.
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Vincent Wenchen Zheng, Bin Cao, Yu Zheng, Xing Xie, and Qiang Yang · 2010
Earlier work this paper cites.
Collaborative filtering with personalized skylines
Ilaria Bartolini, Zhenjie Zhang, and Dimitris Papadias · 2011
Earlier work this paper cites.
Practical variational inference for neural networks
Alex Graves · 2011
Earlier work this paper cites.
Wisdom of the better few: cold start recommendation via representative based rating elicitation
Nathan Nan Liu, Xiangrui Meng, Chao Liu, and Qiang Yang · 2011
Earlier work this paper cites.
Deep belief networks using discriminative features for phone recognition
Abdel-rahman Mohamed, Tara N. Sainath, George E. Dahl, Bhuvana Ramabhadran, Geoffrey E. Hinton, and Michael A. Picheny · 2011
Earlier work this paper cites.
Mcmc using hamiltonian dynamics
Radford M Neal et al · 2011
Earlier work this paper cites.
Introduction to Recommender Systems Handbook
Francesco Ricci, Lior Rokach, and Bracha Shapira · 2011
Earlier work this paper cites.
Contractive auto-encoders: Explicit invariance during feature extraction
Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, and Yoshua Bengio · 2011
Earlier work this paper cites.
Collaborative topic modeling for recommending scientific articles
Chong Wang and David M. Blei · 2011
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Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee Whye Teh · 2011
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Gediminas Adomavicius and YoungOk Kwon · 2012
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Minmin Chen, Zhixiang Eddie Xu, Kilian Q. Weinberger, and Fei Sha · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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