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[1] Lukás Burget,et al. Recurrent neural network based language model , 2010, INTERSPEECH.
[2] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Arthur Szlam,et al. Automatic Rule Extraction from Long Short Term Memory Networks , 2016, ICLR.
[4] Sergey Levine,et al. High-Dimensional Continuous Control Using Generalized Advantage Estimation , 2015, ICLR.
[5] Nicholas Mattei,et al. A Natural Language Argumentation Interface for Explanation Generation in Markov Decision Processes , 2011, ExaCt.
[6] Shie Mannor,et al. Graying the black box: Understanding DQNs , 2016, ICML.
[7] Alexander Binder,et al. Explaining nonlinear classification decisions with deep Taylor decomposition , 2015, Pattern Recognit..
[8] Andrew Zisserman,et al. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps , 2013, ICLR.
[9] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[10] F. Elizalde,et al. Policy Explanation in Factored Markov Decision Processes , 2008 .
[11] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[12] Zhe L. Lin,et al. Top-Down Neural Attention by Excitation Backprop , 2016, International Journal of Computer Vision.
[13] Fei-Fei Li,et al. Deep visual-semantic alignments for generating image descriptions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Marc G. Bellemare,et al. The Arcade Learning Environment: An Evaluation Platform for General Agents (Extended Abstract) , 2012, IJCAI.
[15] Fei-Fei Li,et al. Visualizing and Understanding Recurrent Networks , 2015, ArXiv.
[16] Pascal Poupart,et al. Minimal Sufficient Explanations for Factored Markov Decision Processes , 2009, ICAPS.
[17] Demis Hassabis,et al. Mastering the game of Go without human knowledge , 2017, Nature.
[18] Andrea Vedaldi,et al. Interpretable Explanations of Black Boxes by Meaningful Perturbation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[19] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[20] Tom Schaul,et al. Dueling Network Architectures for Deep Reinforcement Learning , 2015, ICML.
[21] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[22] Thomas Brox,et al. Striving for Simplicity: The All Convolutional Net , 2014, ICLR.
[23] Navdeep Jaitly,et al. Multi-task Neural Networks for QSAR Predictions , 2014, ArXiv.
[24] Carlos Guestrin,et al. "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.
[25] Bradley Hayes,et al. Improving Robot Controller Transparency Through Autonomous Policy Explanation , 2017, 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI.
[26] Alex Graves,et al. Asynchronous Methods for Deep Reinforcement Learning , 2016, ICML.
[27] Avanti Shrikumar,et al. Learning Important Features Through Propagating Activation Differences , 2017, ICML.
[28] Yarin Gal,et al. Real Time Image Saliency for Black Box Classifiers , 2017, NIPS.