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Showing below up to 100 results in range #251 to #350.

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  1. f15Stat946PaperSignUp
  2. f17Stat946PaperSignUp
  3. from Machine Learning to Machine Reasoning
  4. generating Random Numbers
  5. generating text with recurrent neural networks
  6. genetics
  7. goingDeeperWithConvolutions
  8. graph Laplacian Regularization for Larg-Scale Semidefinite Programming
  9. graphical models for structured classification, with an application to interpreting images of protein subcellular location patterns
  10. graves et al., Speech recognition with deep recurrent neural networks
  11. hamming Distance Metric Learning
  12. hierarchical Dirichlet Processes
  13. human-level control through deep reinforcement learning
  14. imageNet Classification with Deep Convolutional Neural Networks
  15. importance Sampling June 2 2009
  16. importance Sampling and Markov Chain Monte Carlo (MCMC)
  17. importance Sampling and Monte Carlo Simulation
  18. incremental Learning, Clustering and Hierarchy Formation of Whole Body Motion Patterns using Adaptive Hidden Markov Chains(Summary)
  19. independent Component Analysis: algorithms and applications
  20. inductive Kernel Low-rank Decomposition with Priors: A Generalized Nystrom Method
  21. infoboxtest
  22. is Multinomial PCA Multi-faceted Clustering or Dimensionality Reduction
  23. joint training of a convolutional network and a graphical model for human pose estimation
  24. kernel Dimension Reduction in Regression
  25. kernel Spectral Clustering for Community Detection in Complex Networks
  26. kernelized Locality-Sensitive Hashing
  27. kernelized Sorting
  28. large-Scale Supervised Sparse Principal Component Analysis
  29. learn what not to learn
  30. learning2reasoning
  31. learning Convolutional Feature Hierarchies for Visual Recognition
  32. learning Fast Approximations of Sparse Coding
  33. learning Hierarchical Features for Scene Labeling
  34. learning Long-Range Vision for Autonomous Off-Road Driving
  35. learning Phrase Representations
  36. learning Spectral Clustering, With Application To Speech Separation
  37. learning a Nonlinear Embedding by Preserving Class Neighborhood Structure
  38. link to my paper
  39. mULTIPLE OBJECT RECOGNITION WITH VISUAL ATTENTION
  40. main Page
  41. mark Your Contribution here
  42. mark your contribution here
  43. markov Chain Definitions
  44. markov Random Fields for Super-Resolution
  45. matrix Completion with Noise
  46. maximum-Margin Matrix Factorization
  47. maximum Variance Unfolding (June 2 2009)
  48. maximum likelihood estimation of intrinsic dimension
  49. meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting
  50. measuring Statistical Dependence with Hilbert-Schmidt Norm
  51. measuring and testing dependence by correlation of distances
  52. measuring statistical dependence with Hilbert-Schmidt norms
  53. memory Networks
  54. metric and Kernel Learning Using a Linear Transformation
  55. monte Carlo Integration
  56. monte Carlo methods
  57. multi-Task Feature Learning
  58. natural language processing (almost) from scratch.
  59. neighbourhood Components Analysis
  60. neural Machine Translation: Jointly Learning to Align and Translate
  61. neural Turing Machines
  62. nonlinear Dimensionality Reduction by Semidefinite Programming and Kernel Matrix Factorization
  63. nonparametric Latent Feature Models for Link Prediction
  64. on the Number of Linear Regions of Deep Neural Networks
  65. on the difficulty of training recurrent neural networks
  66. on using very large target vocabulary for neural machine translation
  67. optimal Solutions forSparse Principal Component Analysis
  68. orthogonal gradient descent for continual learning
  69. overfeat: integrated recognition, localization and detection using convolutional networks
  70. paper 13
  71. paper Summaries
  72. parametric Local Metric Learning for Nearest Neighbor Classification
  73. parsing natural scenes and natural language with recursive neural networks
  74. policy optimization with demonstrations
  75. positive Semidefinite Metric Learning Using Boosting-like Algorithms
  76. probabilistic Matrix Factorization
  77. probabilistic PCA with GPLVM
  78. proof
  79. proof of Lemma 1
  80. proof of Theorem 1
  81. proposal Fall 2010
  82. proposal for STAT946 (Deep Learning) final projects Fall 2015
  83. proposal for STAT946 projects
  84. proposal for STAT946 projects Fall 2010
  85. quantifying cancer progression with conjunctive Bayesian networks
  86. quantifying cancer progression with conjunctive Bayesian networks.
  87. question Answering with Subgraph Embeddings
  88. rOBPCA: A New Approach to Robust Principal Component Analysis
  89. regression on Manifold using Kernel Dimension Reduction
  90. relevant Component Analysis
  91. residual Component Analysis: Generalizing PCA for more flexible inference in linear-Gaussian models
  92. s13Stat946proposal
  93. sandbox to test w2l
  94. scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers Machines
  95. schedule
  96. schedule946
  97. schedule of Project Presentations
  98. self-Taught Learning
  99. semi-supervised Learning with Deep Generative Models
  100. show, Attend and Tell: Neural Image Caption Generation with Visual Attention

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