Deep Neural Decision Forests
暂无分享,去创建一个
Peter Kontschieder | Antonio Criminisi | Samuel Rota Bulò | Madalina Fiterau | A. Criminisi | P. Kontschieder | M. Fiterau | S. R. Bulò
[1] Wayne Ieee,et al. Entropy Nets: From Decision Trees to Neural Networks , 1990 .
[2] P. W. Frey,et al. Letter recognition using Holland-style adaptive classifiers , 2004, Machine Learning.
[3] Simon Kasif,et al. Induction of Oblique Decision Trees , 1993, IJCAI.
[4] Martin A. Riedmiller,et al. A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.
[5] Robert A. Jacobs,et al. Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.
[6] Jonathan J. Hull,et al. A Database for Handwritten Text Recognition Research , 1994, IEEE Trans. Pattern Anal. Mach. Intell..
[7] Simon Kasif,et al. A System for Induction of Oblique Decision Trees , 1994, J. Artif. Intell. Res..
[8] Thomas G. Dietterich. What is machine learning? , 2020, Archives of Disease in Childhood.
[9] Yali Amit,et al. Shape Quantization and Recognition with Randomized Trees , 1997, Neural Computation.
[10] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[11] Alberto Suárez,et al. Globally Optimal Fuzzy Decision Trees for Classification and Regression , 1999, IEEE Trans. Pattern Anal. Mach. Intell..
[12] Simon Haykin,et al. GradientBased Learning Applied to Document Recognition , 2001 .
[13] Christopher M. Bishop,et al. Bayesian Hierarchical Mixtures of Experts , 2002, UAI.
[14] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[15] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[16] David J. Slate,et al. Letter Recognition Using Holland-Style Adaptive Classifiers , 1991, Machine Learning.
[17] Mikhail Belkin,et al. Beyond the point cloud: from transductive to semi-supervised learning , 2005, ICML.
[18] Andrew Zisserman,et al. Image Classification using Random Forests and Ferns , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[19] Roberto Cipolla,et al. Segmentation and Recognition Using Structure from Motion Point Clouds , 2008, ECCV.
[20] Rich Caruana,et al. An empirical evaluation of supervised learning in high dimensions , 2008, ICML '08.
[21] Manik Varma,et al. Character Recognition in Natural Images , 2009, VISAPP.
[22] Yoshua Bengio,et al. DECISION TREES DO NOT GENERALIZE TO NEW VARIATIONS , 2010, Comput. Intell..
[23] Sebastian Nowozin,et al. Loss-Specific Training of Non-Parametric Image Restoration Models: A New State of the Art , 2012, ECCV.
[24] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[25] Klaus-Robert Müller,et al. Efficient BackProp , 2012, Neural Networks: Tricks of the Trade.
[26] Ross B. Girshick,et al. Efficient Human Pose Estimation from Single Depth Images , 2013, IEEE Trans. Pattern Anal. Mach. Intell..
[27] Andrew Blake,et al. Efficient Human Pose Estimation from Single Depth Images , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[28] Dimitris N. Metaxas,et al. Entanglement and Differentiable Information Gain Maximization , 2013 .
[29] Aaron Q. Li,et al. Parameter Server for Distributed Machine Learning , 2013 .
[30] Antonio Criminisi,et al. Decision Forests for Computer Vision and Medical Image Analysis , 2013, Advances in Computer Vision and Pattern Recognition.
[31] Peter Kontschieder,et al. GeoF: Geodesic Forests for Learning Coupled Predictors , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[32] Horst Bischof,et al. Alternating Decision Forests , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[33] Dong Yu,et al. Automatic Speech Recognition: A Deep Learning Approach , 2014 .
[34] Alexander J. Smola,et al. Scaling Distributed Machine Learning with the Parameter Server , 2014, OSDI.
[35] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[36] Qiang Chen,et al. Network In Network , 2013, ICLR.
[37] Peter Kontschieder,et al. Neural Decision Forests for Semantic Image Labelling , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[38] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[39] Relating Cascaded Random Forests to Deep Convolutional Neural Networks for Semantic Segmentation , 2015, ArXiv.
[40] Andrea Vedaldi,et al. MatConvNet: Convolutional Neural Networks for MATLAB , 2014, ACM Multimedia.
[41] Jian Sun,et al. Global refinement of random forest , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[42] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[43] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[44] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[45] Fei-Fei Li,et al. Deep visual-semantic alignments for generating image descriptions , 2015, CVPR.
[46] Christian Borgelt,et al. Computational Intelligence , 2016, Texts in Computer Science.
[47] Fei-Fei Li,et al. Deep visual-semantic alignments for generating image descriptions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).