1.1 Deep Learning Hardware: Past, Present, and Future
暂无分享,去创建一个
[1] E. Garst,et al. Birth , 1954 .
[2] F. K. Becker,et al. Automatic equalization for digital communication , 1965 .
[3] Marvin Minsky,et al. Perceptrons: An Introduction to Computational Geometry , 1969 .
[4] Geoffrey E. Hinton,et al. OPTIMAL PERCEPTUAL INFERENCE , 1983 .
[5] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[6] Lawrence D. Jackel,et al. Artificial neural networks for computing , 1986 .
[7] Lawrence D. Jackel,et al. Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.
[8] Lawrence D. Jackel,et al. Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.
[9] Patrick Gallinari,et al. A Framework for the Cooperation of Learning Algorithms , 1990, NIPS.
[10] R. Lee,et al. Reconfigurable Neural Net Chip with 32K Connections , 1990, NIPS.
[11] Lawrence D. Jackel,et al. An analog neural network processor with programmable topology , 1991 .
[12] Lawrence D. Jackel,et al. Application of the ANNA neural network chip to high-speed character recognition , 1992, IEEE Trans. Neural Networks.
[13] Steven Pigeon,et al. VIP: an FPGA-based processor for image processing and neural networks , 1996, Proceedings of Fifth International Conference on Microelectronics for Neural Networks.
[14] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[15] John J. Hopfield,et al. Neural networks and physical systems with emergent collective computational abilities , 1999 .
[16] A. ADoefaa,et al. ? ? ? ? f ? ? ? ? ? , 2003 .
[17] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[18] Jason Weston,et al. A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.
[19] Yann LeCun,et al. An FPGA-based stream processor for embedded real-time vision with Convolutional Networks , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.
[20] Gert Cauwenberghs,et al. Neuromorphic Silicon Neuron Circuits , 2011, Front. Neurosci.
[21] Berin Martini,et al. Large-Scale FPGA-based Convolutional Networks , 2011 .
[22] Jason Weston,et al. Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..
[23] P. Cochat,et al. Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.
[24] Bernabe Linares-Barranco,et al. Comparison between Frame-Constrained Fix-Pixel-Value and Frame-Free Spiking-Dynamic-Pixel ConvNets for Visual Processing , 2012, Front. Neurosci..
[25] Tara N. Sainath,et al. FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .
[26] Yann LeCun,et al. Scene parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers , 2012, ICML.
[27] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[28] Yann LeCun,et al. Pedestrian Detection with Unsupervised Multi-stage Feature Learning , 2012, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[29] Steve B. Furber,et al. The SpiNNaker Project , 2014, Proceedings of the IEEE.
[30] R. Fergus,et al. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.
[31] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[32] Jason Weston,et al. End-To-End Memory Networks , 2015, NIPS.
[33] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[34] Yann LeCun,et al. Deep multi-scale video prediction beyond mean square error , 2015, ICLR.
[35] Eugenio Culurciello,et al. An Analysis of Deep Neural Network Models for Practical Applications , 2016, ArXiv.
[36] Jason Weston,et al. Key-Value Memory Networks for Directly Reading Documents , 2016, EMNLP.
[37] Sergey Levine,et al. Unsupervised Learning for Physical Interaction through Video Prediction , 2016, NIPS.
[38] Pierre Vandergheynst,et al. Geometric Deep Learning: Going beyond Euclidean data , 2016, IEEE Signal Process. Mag..
[39] Gorjan Alagic,et al. #p , 2019, Quantum information & computation.
[40] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[41] Kaiming He,et al. Focal Loss for Dense Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[42] Tomas Mikolov,et al. Bag of Tricks for Efficient Text Classification , 2016, EACL.
[43] V. Sze,et al. Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks , 2016, IEEE Journal of Solid-State Circuits.
[44] Trevor Darrell,et al. Learning to Reason: End-to-End Module Networks for Visual Question Answering , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[45] Kaiming He,et al. Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[46] Kaiming He,et al. Exploring the Limits of Weakly Supervised Pretraining , 2018, ECCV.
[47] Kilian Q. Weinberger,et al. Multi-Scale Dense Networks for Resource Efficient Image Classification , 2017, ICLR.
[48] Jeff Johnson,et al. Rethinking floating point for deep learning , 2018, ArXiv.
[49] Jaakko Lehtinen,et al. Progressive Growing of GANs for Improved Quality, Stability, and Variation , 2017, ICLR.
[50] Yann LeCun,et al. A Closer Look at Spatiotemporal Convolutions for Action Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[51] Vivienne Sze,et al. Eyeriss v2: A Flexible and High-Performance Accelerator for Emerging Deep Neural Networks , 2018, ArXiv.
[52] Myle Ott,et al. Scaling Neural Machine Translation , 2018, WMT.
[53] Albert Cohen,et al. Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions , 2018, ArXiv.
[54] Yann LeCun,et al. Predicting Future Instance Segmentations by Forecasting Convolutional Features , 2018, ECCV.
[55] Douwe Kiela,et al. Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry , 2018, ICML.
[56] Jeff Donahue,et al. Large Scale GAN Training for High Fidelity Natural Image Synthesis , 2018, ICLR.
[57] Yann LeCun,et al. Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic , 2019, ICLR.
[58] Volker Tresp. The Perceptron , 2019, Principles of Artificial Neural Networks.