Novel deep generative simultaneous recurrent model for efficient representation learning

Author:

Alam M.,Vidyaratne L.,Iftekharuddin K.M.

Funder

National Science Foundation (NSF), United States

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference53 articles.

1. Alam, M., Vidyaratne, L., & Iftekharuddin, K. M. (2016). Efficient feature extraction with simultaneous recurrent network for metric learning. In 2016 international joint conference on neural networks (IJCNN) (pp. 1195–1201).

2. Sparse simultaneous recurrent deep learning for robust facial expression recognition;Alam;IEEE Transactions on Neural Networks and Learning Systems,2018

3. Deep SRN for robust object recognition: A case study with NAO humanoid robot;Alam,2016

4. Bengio, Y., Yao, L., & Cho, K. (2013). Bounding the test log-likelihood of generative models. arXiv preprint arXiv:1311.6184.

5. Boulanger-Lewandowski, N., Bengio, Y., & Vincent, P. (2012). Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription. arXiv preprint arXiv:1206.6392.

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