Interpretable Structure-Evolving LSTM

@article{Liang2017InterpretableSL,
  title={Interpretable Structure-Evolving LSTM},
  author={Xiaodan Liang and Liang Lin and Xiaohui Shen and Jiashi Feng and Shuicheng Yan and Eric P. Xing},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  pages={2175-2184}
}
This paper develops a general framework for learning interpretable data representation via Long Short-Term Memory (LSTM) recurrent neural networks over hierarchal graph structures. Instead of learning LSTM models over the pre-fixed structures, we propose to further learn the intermediate interpretable multi-level graph structures in a progressive and stochastic way from data during the LSTM network optimization. We thus call this model the structure-evolving LSTM. In particular, starting with… CONTINUE READING
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