Iterative Neural Autoregressive Distribution Estimator NADE-kIterative Neural Autoregressive Distribution Estimator NADE-kRaiko, Tapani and Li, Yao and Cho, KyungHyun and Bengio, Yoshua2014
Paper summarycubs#### Problem addressed:
Fully visible Bayesian network learning
#### Summary:
This paper is very similar to the order agnostic NADE paper, it generalized the idea of order agnostic NADE and extended to k iterations. The difference between this work and the previous NADE work is: 1, instead of totally mask out the variables to compute, it instead provide the data mean for those variables; 2. mask is not supplied to the network; 3. it employed a walk-back like scheme, where the prediction is completed in k iterations.
#### Novelty:
It is a generalization of NADE models.
#### Drawbacks:
Training would be slow, and with large k, the challenge of training very deep net remains.
#### Datasets:
binary mnist, caltec-101 silhouettes
#### Additional remarks:
#### Resources:
implementation is at https://github.com/yaoli/nade_k
#### Presenter:
Yingbo Zhou
#### Problem addressed:
Fully visible Bayesian network learning
#### Summary:
This paper is very similar to the order agnostic NADE paper, it generalized the idea of order agnostic NADE and extended to k iterations. The difference between this work and the previous NADE work is: 1, instead of totally mask out the variables to compute, it instead provide the data mean for those variables; 2. mask is not supplied to the network; 3. it employed a walk-back like scheme, where the prediction is completed in k iterations.
#### Novelty:
It is a generalization of NADE models.
#### Drawbacks:
Training would be slow, and with large k, the challenge of training very deep net remains.
#### Datasets:
binary mnist, caltec-101 silhouettes
#### Additional remarks:
#### Resources:
implementation is at https://github.com/yaoli/nade_k
#### Presenter:
Yingbo Zhou