CV書(shū)單-Benjio PAMI review (up tp 2013)

Collected from a net friend’s blog:

Review Book List:

• [2009 Thesis] Learning Deep Generative Models.pdf
• [2009] Learning Deep Architectures for AI.pdf
• [2013 DengLi Review] Deep Learning for Signal and Information
  Processing.pdf http://deeplearning.net/tutorial/deeplearning.pdf

Paper List:

• [1996 Nature] sparse coding.pdf
• [1997 Vision] Sparse coding with an overcomplete basis set.pdf
• [1998 NIPS] EM Algorithms for PCA and SPCA.pdf
• [1998 PIEEE] Gradient-Based Learning Applied to Document
  Recognition.pdf
• [1999] Probabilistic Principal Component Analysis.pdf
• [2002 NC] Training Products of Experts by Minimizing Contrastive
  Divergence.pdf
• [2005 JMLR] Estimation of non-normalized statistical models by score
  matching.pdf
• [2006 NC] A fast learning algorithm for deep belief nets.pdf
• [2006 NIPS] Efficient Learning of Sparse Representations with an
  Energy-Based Model.pdf
• [2006 NIPS] Efficient sparse coding algorithms.pdf
• [2006 Science] Reducing the Dimensionality of Data with Neural
  Networks.pdf
• [2006] A Tutorial on Energy-Based Learning.pdf
• [2006] To Recognize Shapes, First Learn to Generate Images
  montrealTR.pdf
• [2007 BOOK] Scaling Learning Algorithms towards AI.pdf
• [2007 CVPR] Unsupervised Learning of Invariant Feature Hierarchies
  with Applications to Object Recognition.pdf
• [2007 ICML] Self-taught learning transfer learning from unlabeled
  data.pdf
• [2007 NIPS TR] Greedy Layer-Wise Training of Deep Networks.pdf
• [2007 NIPS] Sparse deep belief net model for visual area V2.pdf
• [2007 NIPS] Sparse Feature Learning for Deep Belief Networks.pdf
• [2007] Energy-Based Models in Document Recognition and Computer
  Vision.pdf
• [2008 ICML] Extracting and Composing Robust Features with Denoising
  Autoencoders.pdf
• [2008 ICML] Training restricted Boltzmann machines using
  approximations to the likelihood gradient.pdf
• [2008 PSD] Fast Inference in Sparse Coding Algorithms with
  Applications to Object Recognition.pdf
• [2009 AISTATS] Deep Boltzmann Machines.pdf
• [2009 CVPR] Learning invariant features through topographic filter
  maps.pdf
• [2009 CVPR] Linear Spatial Pyramid Matching Using Sparse Coding for
  Image Classification.pdf
• [2009 ICCV] What is the Best Multi-Stage Architecture for Object
  Recognition.pdf
• [2009 ICML] Using Fast Weights to Improve Persistent Contrastive
  Divergence.pdf
• [2009 JMLR] Exploring Strategies for Training Deep Neural
  Networks.pdf
• [2009 NIPS] Nonlinear Learning using Local Coordinate Coding.pdf
• [2010 AISTATS] Efficient Learning of Deep Boltzmann Machines.pdf
• [2010 AISTATS] On the convergence properties of contrastive
  divergence.pdf
• [2010 CVPR] Learning Mid-Level Features For Recognition.pdf
• [2010 CVPR] Locality-constrained Linear Coding for Image
  Classification.pdf
• [2010 CVPR] Modeling Pixel Means and Covariances Using Factorized
  Third-Order Boltzmann Machines.pdf
• [2010 ECCV] Image classification using super-vector coding of local
  image descriptors.pdf
• [2010 ICML] A Theoretical Analysis of Feature Pooling in Visual
  Recognition.pdf
• [2010 ICML] Deep learning via Hessian-free optimization.pdf
• [2010 ICML] Learning Deep Boltzmann Machines using Adaptive MCMC.pdf
• [2010 ISCAS] Convolutional Networks and Applications in Vision.pdf
• [2010 JMLR] Stacked Denoising Autoencoders Learning Useful
  Representations.pdf
• [2010 JMLR] Why Does Unsupervised Pre-training Help Deep Learning.pdf
• [2010 NIPS] Learning Convolutional Feature Hierarchies for Visual
  Recognition.pdf
• [2010 NIPS] Regularized estimation of image statistics by Score
  Matching.pdf
• [2011 CACM] Unsupervised Learning of Hierarchical Representations
  with Convolutional Deep Belief Networks.pdf
• [2011 CVPR] Learning image representations from the pixel level via
  hierarchical sparse coding.pdf
• [2011 ICCV] Adaptive Deconvolutional Networks for Mid and High Level
  Feature Learning.pdf
• [2011 ICML] Contractive Auto-Encoders.pdf
• [2011 ICML] Learning Deep Energy Models.pdf
• [2011 ICML] On Autoencoders and Score Matching for Energy Based
  Models.pdf
• [2011 ICML] On optimization methods for deep learning.pdf
• [2011 ICML] Unsupervised Models of Images by Spike-and-Slab RBMs.pdf
• [2011 JMLR] Unsupervised and transfer learning challenge a deep
  learning approach.pdf
• [2011 NC] A Connection Between Score Matching and Denoising
  Autoencoders.pdf
• [2011 NIPS] Algorithms for Hyper-Parameter Optimization.pdf
• [2011 NIPS] Spike-and-Slab Sparse Coding for Unsupervised Feature
  Discovery.pdf
• [2011 UAI] Asymptotic efficiency of deterministic estimators for
  discrete energy-based models Ratio matching and pseudolikelihood.pdf
• [2011] On the Expressive Power of Deep Architectures.pdf
• [2012 Book] A Practical Guide to Training Restricted Boltzmann
  Machines.pdf
• [2012 Dropout] Improving neural networks by preventing co-adaptation
  of feature detectors.pdf
• [2012 ICML] A Generative Process for Sampling Contractive
  Auto-Encoders.pdf
• [2012 ICML] Building High-level Features Using Large Scale
  Unsupervised Learning.pdf
• [2012 ICML] Large-Scale Feature Learning With Spike-and-Slab Sparse
  Coding.pdf
• [2012 JMLR] Random Search for Hyper-Parameter Optimization.pdf
• [2012 NC] An Efficient Learning Procedure for Deep Boltzmann
  Machines.pdf
• [2012 NIPS] A Better Way to Pre-Train Deep Boltzmann Machines.pdf
• [2012 NIPS] Discriminative Learning of Sum-Product Networks.pdf
• [2012 NIPS] ImageNet Classification with Deep Convolutional Neural
  Networks.pdf
• [2012 NIPS] Practical Bayesian Optimization of Machine Learning
  Algorithms.pdf
• [2012] Deep Learning via Semi-Supervised Embedding.pdf
• [2013 BOOK] Deep Learning of Representations.pdf
• [2013 ICLR] Stochastic Pooling for Regularization of Deep
  Convolutional Neural Networks.pdf
• [2013 ICLR] What Regularized Auto-Encoders Learn from the Data
  Generating Distribution.pdf
• [2013 ICML] Better Mixing via Deep Representations.pdf
• [2013 ICML] No more pesky learning rates.pdf
• [2013 ICML] On autoencoder scoring.pdf
• [2013 ICML] On the importance of initialization and momentum in deep
  learning.pdf
• [2013 ICML] Regularization of Neural Networks using DropConnect.pdf
• [2013 NIPS] Adaptive dropout for training deep neural networks.pdf
• [2013 NIPS] Deep Fisher Networks for Large-Scale Image
  Classification.pdf
• [2013 NIPS] Deep Neural Networks for Object Detection.pdf
• [2013 NIPS] Dropout Training as Adaptive Regularization.pdf
• [2013 NIPS] Generalized Denoising Auto-Encoders as Generative
  Models.pdf
• [2013 NIPS] Learning a Deep Compact Image Representation for Visual
  Tracking.pdf
• [2013 NIPS] Learning Multi-level Sparse Representations.pdf
• [2013 NIPS] Understanding Dropout.pdf
• [2013 PAMI] Deep Hierarchies in the Primate Visual Cortex What Can We
  Learn For Computer Vision.pdf
• [2013 PAMI] Deep Learning with Hierarchical Convolutional Factor
  Analysis.pdf
• [2013 PAMI] Invariant Scattering Convolution Networks.pdf
• [2013 PAMI] Learning Hierarchical Features for Scene Labeling.pdf
• [2013 PAMI] Learning with Hierarchical-Deep Models.pdf
• [2013 PAMI] Representation Learning A Review and New Perspectives.pdf
• [2013 PAMI] Scaling Up Spike-and-Slab Models for Unsupervised Feature
  Learning.pdf
• [2013 TR] Maxout networks.pdf
• [2013 TR] Practical recommendations for gradient-based training of
  deep architectures.pdf
• [2013] Network in Network.pdf
• [2013] Visualizing and Understanding Convolutional Networks.pdf
• Presentation List:
• 2007 Deep Belief Nets by hinton on nips2007.pdf
• 2009 Learning Deep Architectures by Yoshua Bengio.pdf
• 2010 Tutorial on Deep Learning and Applications by Honglak Lee on
  nips2010 workshop.pdf
• 2010 Unsupervised Learning by ranzato on nips2010 workshop.pdf
• 2012 A Tutorial on Deep Learning by yukai.pdf
• 2012 Deep Learning Methods for Vision on cvpr2012.pdf
• 2013 Deep Learning for Computer Vision by Rob Fergus on icml2013.pdf
• 2013 Deep Learning for Vision Tricks of the Trade by ranzato on
  bavm2013.pdf
• 2013 Deep Learning of Representations by Yoshua Bengio on
  aaai2013.pdf
• 2013 Deep Learning of Representations by Yoshua Bengio on
  sstic2013.pdf
• 2013 Deep Learning Tutorial by lecun && ranzato on icml2013.pdf
• 2013 Large-Scale Visual Recognition With Deep Learning by ranzato on
  cvpr2013.pdf
• 2013 Recent Advances in Deep Learning by Kevin Duh.pdf
• 2013 Recent Developments in Deep Neural Networks by hinton on
  icassp2013.pdf
• DeepLearning_SummerSchool\2012 Advanced Hierarchical Models by
  Salakhutdinov on ipam2012.pdf
• DeepLearning_SummerSchool\2012 An Algebraic Perspective on Deep
  Learning on ipam2012.pdf
• DeepLearning_SummerSchool\2012 An Informal Mathematical Tour of
  Feature Learning on ipam2012.pdf
• DeepLearning_SummerSchool\2012 Deep Gated MRF’s on ipam2012.pdf
• DeepLearning_SummerSchool\2012 Deep Learning & Feature Learning
  Methods for Vision on ipam2012.pdf
• DeepLearning_SummerSchool\2012 Deep learning in the visual cortex on
  ipam2012.pdf
• DeepLearning_SummerSchool\2012 Deep Learning Tutorial by hinton on
  ipam2012.pdf
• DeepLearning_SummerSchool\2012 Deep Learning, Graphical Models,
  EnergyBased Models, Structured Prediction by LeCun on ipam2012.pdf
• DeepLearning_SummerSchool\2012 From natural scene statistics to
  models of neural coding and representation on ipam2012.pdf
• DeepLearning_SummerSchool\2012 Introduction to MCMC for Deep Learning
  on ipam 2012.pdf
• DeepLearning_SummerSchool\2012 Large-Scale Deep Learning on
  ipam2012.pdf
• DeepLearning_SummerSchool\2012 Learning Hierarchical Generative
  Models on ipam2012.pdf
• DeepLearning_SummerSchool\2012 Learning Hierarchies of Invariant
  Features by LeCun on ipam 2012.pdf
• DeepLearning_SummerSchool\2012 Machine Learning and AI via Brain
  simulations by Andrew Ng on ipam2012.pdf
• DeepLearning_SummerSchool\2012 Multiview Feature Learning on
  ipam2012.pdf
• DeepLearning_SummerSchool\2012 Neural Networks Representation
  Non-linear hypotheses on ipam2012.pdf
• DeepLearning_SummerSchool\2012 Scattering Invariant Deep Networks for
  Classification by Mallat on ipam2012.pdf

---

其內(nèi)容主要截取自 Benjio 在 2013 年發(fā)表的一篇文章：

Representation Learning: A Review and New Perspectives

這里主要列出了有關(guān) Vision 的文章，而語(yǔ)音、圖像和自然語(yǔ)言處理并沒(méi)有羅列。

另外這個(gè)地址可能需要翻墻，需要的同學(xué)自己來(lái)找吧。—>免費(fèi)翻墻合集

總結(jié)

以上是生活随笔為你收集整理的CV书单-Benjio PAMI review (up tp 2013)的全部?jī)?nèi)容，希望文章能夠幫你解決所遇到的問(wèn)題。

如果覺(jué)得生活随笔網(wǎng)站內(nèi)容還不錯(cuò)，歡迎將生活随笔推薦給好友。