Fetching the paper…
Reading the bibliography…
This paper proposes CF-NADE, a neural autoregressive architecture for collaborative filtering (CF) tasks, which is inspired by the Restricted Boltzmann Machine (RBM) based CF model and the Neural Autoregressive Distribution Estimator (NADE).
Grouplens: an open architecture for collaborative filtering of netnews
Resnick, Paul, Iacovou, Neophytos, Suchak, Mitesh, Bergstrom, Peter, and Riedl, John · 1994
Earlier work this paper cites.
Learning collaborative information filters
Billsus, Daniel and Pazzani, Michael J · 1998
Earlier work this paper cites.
Fast maximum margin matrix factorization for collaborative prediction
Rennie, Jasson DM and Srebro, Nathan · 2005
Earlier work this paper cites.
The netflix prize
Bennett, James and Lanning, Stan · 2007
Earlier work this paper cites.
Probabilistic matrix factorization
Mnih, Andriy and Salakhutdinov, Ruslan · 2007
Earlier work this paper cites.
Restricted boltzmann machines for collaborative filtering
Salakhutdinov, Ruslan, Mnih, Andriy, and Hinton, Geoffrey · 2007
Earlier work this paper cites.
Collaborative filtering for implicit feedback datasets
Hu, Yifan, Koren, Yehuda, and Volinsky, Chris · 2008
Earlier work this paper cites.
Bayesian probabilistic matrix factorization using markov chain monte carlo
Salakhutdinov, Ruslan and Mnih, Andriy · 2008
Earlier work this paper cites.
Listwise approach to learning to rank: theory and algorithm
Xia, Fen, Liu, Tie-Yan, Wang, Jue, Zhang, Wensheng, and Li, Hang · 2008
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Koren, Yehuda, Bell, Robert, and Volinsky, Chris · 2009
Earlier work this paper cites.
Non-linear matrix factorization with gaussian processes
Lawrence, Neil D and Urtasun, Raquel · 2009
Earlier work this paper cites.
Ordinal boltzmann machines for collaborative filtering
Truyen, Tran The, Phung, Dinh Q, and Venkatesh, Svetha · 2009
Cited alongside, same era.
List-wise learning to rank with matrix factorization for collaborative filtering
Shi, Yue, Larson, Martha, and Hanjalic, Alan · 2010
Cited alongside, same era.
The neural autoregressive distribution estimator
Larochelle, Hugo and Murray, Iain · 2011
Cited alongside, same era.
Divide-and-conquer matrix factorization
Mackey, Lester W, Jordan, Michael I, and Talwalkar, Ameet · 2011
Cited alongside, same era.
Theano: new features and speed improvements
Bastien, Frédéric, Lamblin, Pascal, Pascanu, Razvan, Bergstra, James, Goodfellow, Ian J., Bergeron, Arnaud, Bouchard, Nicolas, and Bengio, Yoshua · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, Nitish, Hinton, Geoffrey, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan · 2014
Later among the works it cites.
Going deeper with convolutions
Szegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott, Anguelov, Dragomir, Erhan, Dumitru, Vanhoucke, Vincent, and Rabinovich, Andrew · 2014
Later among the works it cites.
A deep and tractable density estimator
Uria, Benigno, Murray, Iain, and Larochelle, Hugo · 2014
Later among the works it cites.
Topic modeling of multimodal data: An autoregressive approach
Zheng, Yin, Zhang, Yu-Jin, and Larochelle, H · 2014
Later among the works it cites.
Neural network matrix factorization
Dziugaite, Gintare Karolina and Roy, Daniel M · 2015
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
A neural autoregressive topic model
Larochelle, Hugo and Lauly, Stanislas · 2012
Cited alongside, same era.
Scalable recommendation with poisson factorization
Gopalan, Prem, Hofman, Jake M, and Blei, David M · 2013
Cited alongside, same era.
Local low-rank matrix approximation
Lee, Joonseok, Kim, Seungyeon, Lebanon, Guy, and Singer, Yoram · 2013
Cited alongside, same era.
Rnade: The real-valued neural autoregressive density-estimator
Uria, Benigno, Murray, Iain, and Larochelle, Hugo · 2013
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, Diederik and Ba, Jimmy · 2014
Cited alongside, same era.
Bayesian nonparametric poisson factorization for recommendation systems
Gopalan, Prem, Ruiz, Francisco JR, Ranganath, Rajesh, and Blei, David M
Cited in the paper.
Later among the works it cites.
The movielens datasets: History and context
Harper, F Maxwell and Konstan, Joseph A · 2015
Later among the works it cites.
Deep residual learning for image recognition
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian · 2015
Later among the works it cites.
Autorec: Autoencoders meet collaborative filtering
Sedhain, Suvash, Menon, Aditya Krishna, Sanner, Scott, and Xie, Lexing · 2015
Later among the works it cites.
Blocks and fuel: Frameworks for deep learning
van Merriënboer, Bart, Bahdanau, Dzmitry, Dumoulin, Vincent, Serdyuk, Dmitriy, Warde-Farley, David, Chorowski, Jan, and Bengio, Yoshua · 2015
Later among the works it cites.
A deep and autoregressive approach for topic modeling of multimodal data
Zheng, Y., Zhang, Yu-Jin, and Larochelle, H · 2015
Later among the works it cites.