Deep Learning with Recurrent/Recursive Neural Networks (RNN) – ICLR 2017 Discoveries

The 5th International Conference on Learning Representation (ICLR 2017) is coming to Toulon, France (April 24-26 2017).

This blog post gives an overview of Deep Learning with Recurrent/Recursive Neural Networks (RNN) related papers submitted to ICLR 2017, see underneath for the list of papers. If you want to learn more about RNN check out Andrej Karpathy’s The Unreasonable Effectiveness of Recurrent Neural Networksand Pascanu, Gulcehre, Cho and Bengio’s How to Construct Deep Recurrent Neural Networks.

Best regards,
Amund Tveit

  1. Making Neural Programming Architectures Generalize via Recursion – Authors: Jonathon Cai, Richard Shin, Dawn Song
  2. Multi-label learning with the RNNs for Fashion Search – Authors: Taewan Kim
  3. Recursive Regression with Neural Networks: Approximating the HJI PDE Solution – Authors: Vicenç Rubies Royo
  4. SampleRNN: An Unconditional End-to-End Neural Audio Generation Model – Authors: Soroush Mehri, Kundan Kumar, Ishaan Gulrajani, Rithesh Kumar, Shubham Jain, Jose Manuel Rodriguez Sotelo, Aaron Courville, Yoshua Bengio
  5. Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations – Authors: David Krueger, Tegan Maharaj, Janos Kramar, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Aaron Courville, Christopher Pal
  6. LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation – Authors: Jianwei Yang, Anitha Kannan, Dhruv Batra, Devi Parikh
  7. TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency – Authors: Adji B. Dieng, Chong Wang, Jianfeng Gao, John Paisley

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