582 citations · 1.2k across the 18 of their papers we have counts for
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Ladder Variational Autoencoders
Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe +2
Variational Autoencoders are powerful models for unsupervised learning. However deep models with several layers of dependent stochastic variables are difficult to train which limit…
Linear State-Space Model with Time-Varying Dynamics
Jaakko Luttinen, Tapani Raiko, Alexander Ilin
This paper introduces a linear state-space model with time-varying dynamics. The time dependency is obtained by forming the state dynamics matrix as a time-varying linear combinati…
Techniques for Learning Binary Stochastic Feedforward Neural Networks
Tapani Raiko, Mathias Berglund, Guillaume Alain +1
Stochastic binary hidden units in a multi-layer perceptron (MLP) network give at least three potential benefits when compared to deterministic MLP networks. (1) They allow to learn…
Iterative Neural Autoregressive Distribution Estimator (NADE-k)
Tapani Raiko, Li Yao, Kyunghyun Cho +1
Training of the neural autoregressive density estimator (NADE) can be viewed as doing one step of probabilistic inference on missing values in data. We propose a new model that ext…