Rethinking Motivation of Deep Neural Architectures
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Lei Chen | Jinhu Lu | Weilin Luo | Xuerong Li | Kexin Liu | Jinhu Lu | Xuerong Li | Kexin Liu | Lei Chen | Weilin Luo
[1] Léon Bottou,et al. Towards Principled Methods for Training Generative Adversarial Networks , 2017, ICLR.
[2] JinHu Lü,et al. A threshold effect of coupling delays on intra-layer synchronization in duplex networks , 2018, Science China Technological Sciences.
[3] Léon Bottou,et al. Wasserstein Generative Adversarial Networks , 2017, ICML.
[4] Kunihiko Fukushima,et al. Training multi-layered neural network neocognitron , 2013, Neural Networks.
[5] Xiangyu Zhang,et al. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[6] George Papandreou,et al. Rethinking Atrous Convolution for Semantic Image Segmentation , 2017, ArXiv.
[7] Yi Li,et al. Deformable Convolutional Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[8] Yu Zhang,et al. Simple Recurrent Units for Highly Parallelizable Recurrence , 2017, EMNLP.
[9] Kaiming He,et al. Momentum Contrast for Unsupervised Visual Representation Learning , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Matthew Hutson,et al. Core progress in AI has stalled in some fields. , 2020, Science.
[11] Ole Winther,et al. Autoencoding beyond pixels using a learned similarity metric , 2015, ICML.
[12] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] In-So Kweon,et al. CBAM: Convolutional Block Attention Module , 2018, ECCV.
[14] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Han Zhang,et al. Self-Attention Generative Adversarial Networks , 2018, ICML.
[16] Ian J. Goodfellow,et al. NIPS 2016 Tutorial: Generative Adversarial Networks , 2016, ArXiv.
[17] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[18] Wen Gao,et al. Multiscale Deep Alternative Neural Network for Large-Scale Video Classification , 2018, IEEE Transactions on Multimedia.
[19] Yurong Liu,et al. A survey of deep neural network architectures and their applications , 2017, Neurocomputing.
[20] Pei Wang,et al. Substrate concentration effect on gene expression in genetic circuits with additional positive feedback , 2018, Science China Technological Sciences.
[21] Xiaogang Wang,et al. Residual Attention Network for Image Classification , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Luca Bertinetto,et al. Fully-Convolutional Siamese Networks for Object Tracking , 2016, ECCV Workshops.
[23] Xiong Wang,et al. Identifying topologies and system parameters of uncertain time-varying delayed complex networks , 2018, Science China Technological Sciences.
[24] Hassan Foroosh,et al. Factorized Convolutional Neural Networks , 2016, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).
[25] Francesco Visin,et al. A guide to convolution arithmetic for deep learning , 2016, ArXiv.
[26] Mikael Bodén,et al. A guide to recurrent neural networks and backpropagation , 2001 .
[27] Ming Zhang,et al. Improving the initialization speed for long-range NRTK in network solution mode , 2020 .
[28] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[29] Alekseĭ Grigorʹevich Ivakhnenko,et al. CYBERNETIC PREDICTING DEVICES , 1966 .
[30] Yoshua Bengio,et al. Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.
[31] D. Hubel,et al. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.
[32] Thomas Brox,et al. Synthesizing the preferred inputs for neurons in neural networks via deep generator networks , 2016, NIPS.
[33] Jeffrey L. Elman,et al. Finding Structure in Time , 1990, Cogn. Sci..
[34] H. Bourlard,et al. Auto-association by multilayer perceptrons and singular value decomposition , 1988, Biological Cybernetics.
[35] Sergey Ivanov,et al. Modern Deep Reinforcement Learning Algorithms , 2019, ArXiv.
[36] S. Eddy. Hidden Markov models. , 1996, Current opinion in structural biology.
[37] Yi Tay,et al. Deep Learning based Recommender System: A Survey and New Perspectives , 2018 .
[38] Yoshua Bengio,et al. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.
[39] Enhua Wu,et al. Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[40] Yi Zeng,et al. A Plasticity-Centric Approach to Train the Non-Differential Spiking Neural Networks , 2018, AAAI.
[41] Zachary Chase Lipton. A Critical Review of Recurrent Neural Networks for Sequence Learning , 2015, ArXiv.
[42] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[43] Yoshua Bengio,et al. Unitary Evolution Recurrent Neural Networks , 2015, ICML.
[44] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[45] Geoffrey E. Hinton,et al. A Simple Framework for Contrastive Learning of Visual Representations , 2020, ICML.
[46] Zongli Lin,et al. On PID control for synchronization of complex dynamical network with delayed nodes , 2019, Science China Technological Sciences.
[47] Yoshua Bengio,et al. Deep Sparse Rectifier Neural Networks , 2011, AISTATS.
[48] B.M. Wilamowski,et al. Neural network architectures and learning algorithms , 2009, IEEE Industrial Electronics Magazine.
[49] Chun-Wei Yang,et al. Applications of artificial intelligence in intelligent manufacturing: a review , 2017, Frontiers of Information Technology & Electronic Engineering.
[50] Udayan Ganguly,et al. An On-Chip Trainable and the Clock-Less Spiking Neural Network With 1R Memristive Synapses , 2017, IEEE Transactions on Biomedical Circuits and Systems.
[51] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[52] Simon Osindero,et al. Conditional Generative Adversarial Nets , 2014, ArXiv.
[53] Ming Zhang,et al. An overview on GNSS carrier-phase time transfer research , 2020 .
[54] Jürgen Schmidhuber,et al. Recurrent nets that time and count , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.
[55] Milos Manic,et al. Deep Learning and Reconfigurable Platforms in the Internet of Things: Challenges and Opportunities in Algorithms and Hardware , 2018, IEEE Industrial Electronics Magazine.
[56] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[57] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[58] Mubarak Shah,et al. Real-World Anomaly Detection in Surveillance Videos , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[59] Geoffrey E. Hinton,et al. Dynamic Routing Between Capsules , 2017, NIPS.
[60] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[61] 拓海 杉山,et al. “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .
[62] Wen-Hsien Fang,et al. Improved Object Detection With Iterative Localization Refinement in Convolutional Neural Networks , 2018, IEEE Transactions on Circuits and Systems for Video Technology.
[63] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[64] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[65] Jaakko Lehtinen,et al. Progressive Growing of GANs for Improved Quality, Stability, and Variation , 2017, ICLR.
[66] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.