暂无分享,去创建一个
Ioannis Mitliagkas | Yoshua Bengio | Devansh Arpit | Giancarlo Kerg | Nan Rosemary Ke | Bhargav Kanuparthi | Yoshua Bengio | Ioannis Mitliagkas | Devansh Arpit | Giancarlo Kerg | Bhargav Kanuparthi
[1] Jiebo Luo,et al. Image Captioning with Semantic Attention , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Geoffrey E. Hinton,et al. A Simple Way to Initialize Recurrent Networks of Rectified Linear Units , 2015, ArXiv.
[3] Ilya Sutskever,et al. Learning Recurrent Neural Networks with Hessian-Free Optimization , 2011, ICML.
[4] Stephen José Hanson,et al. A stochastic version of the delta rule , 1990 .
[5] Samy Bengio,et al. Show and tell: A neural image caption generator , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Vysoké Učení,et al. Statistical Language Models Based on Neural Networks , 2012 .
[7] Yoshua Bengio,et al. Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations , 2016, ICLR.
[8] Christopher Joseph Pal,et al. Twin Networks: Matching the Future for Sequence Generation , 2017, ICLR.
[9] Richard Socher,et al. Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Yoshua Bengio,et al. The problem of learning long-term dependencies in recurrent networks , 1993, IEEE International Conference on Neural Networks.
[11] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[12] Les E. Atlas,et al. Full-Capacity Unitary Recurrent Neural Networks , 2016, NIPS.
[13] Tao Mei,et al. Boosting Image Captioning with Attributes , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[14] Yoshua Bengio,et al. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.
[15] Yoshua Bengio,et al. An empirical analysis of dropout in piecewise linear networks , 2013, ICLR.
[16] Yoshua Bengio,et al. Unitary Evolution Recurrent Neural Networks , 2015, ICML.
[17] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Jürgen Schmidhuber,et al. Learning to Forget: Continual Prediction with LSTM , 2000, Neural Computation.
[19] Yann LeCun,et al. Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs , 2016, ICML.
[20] Yoshua Bengio,et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.
[21] Peter Tiño,et al. Learning long-term dependencies in NARX recurrent neural networks , 1996, IEEE Trans. Neural Networks.
[22] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[23] Shuai Li,et al. Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[24] Inderjit S. Dhillon,et al. Learning Long Term Dependencies via Fourier Recurrent Units , 2018, ICML.
[25] Nan Rosemary Ke,et al. Sparse Attentive Backtracking : Towards Efficient Credit Assignment In Recurrent Networks , 2017 .
[26] Moustapha Cissé,et al. Kronecker Recurrent Units , 2017, ICML.
[27] Yoshua Bengio,et al. Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.
[28] Razvan Pascanu,et al. On the difficulty of training recurrent neural networks , 2012, ICML.
[29] Fei-Fei Li,et al. Deep visual-semantic alignments for generating image descriptions , 2015, CVPR.
[30] Jeffrey L. Elman,et al. Finding Structure in Time , 1990, Cogn. Sci..
[31] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[32] James Bailey,et al. Efficient Orthogonal Parametrisation of Recurrent Neural Networks Using Householder Reflections , 2016, ICML.
[33] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[34] Quoc V. Le,et al. Learning Longer-term Dependencies in RNNs with Auxiliary Losses , 2018, ICML.
[35] Eran Yahav,et al. On the Practical Computational Power of Finite Precision RNNs for Language Recognition , 2018, ACL.