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Showing below up to 100 results in range #201 to #300.
- a Rank Minimization Heuristic with Application to Minimum Order System Approximation
- a fair comparison of graph neural networks for graph classification
- a fast learning algorithm for deep belief nets
- a neural representation of sketch drawings
- adaptive dimension reduction for clustering high dimensional data
- again on Markov Chain
- bayesian and Frequentist Schools of Thought
- binomial Probability Monte Carlo Sampling June 2 2009
- compressive Sensing
- conditional neural process
- consistency of Trace Norm Minimization
- context Adaptive Training with Factorized Decision Trees for HMM-Based Speech Synthesis
- contributions on Context Adaptive Training with Factorized Decision Trees for HMM-Based Speech Synthesis
- contributions on Quantifying Cancer Progression with Conjunctive Bayesian Networks
- contributions on Video-Based Face Recognition Using Adaptive Hidden Markov Models
- copyofstat341
- decentralised Data Fusion: A Graphical Model Approach (Summary)
- deepGenerativeModels
- deep Convolutional Neural Networks For LVCSR
- deep Learning of the tissue-regulated splicing code
- deep neural networks for acoustic modeling in speech recognition
- deflation Method for Penalized Matrix Decomposition Sparse PCA
- deflation Methods for Sparse PCA
- dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces
- discLDA: Discriminative Learning for Dimensionality Reduction and Classification
- distributed Representations of Words and Phrases and their Compositionality
- f11Stat841EditorSignUp
- f11Stat841presentation
- f11Stat841proposal
- f11Stat946papers
- f11Stat946presentation
- f11stat946EditorSignUp
- f14Stat842EditorSignUp
- f15Stat946PaperSignUp
- f17Stat946PaperSignUp
- generating Random Numbers
- genetics
- hamming Distance Metric Learning
- hierarchical Dirichlet Processes
- importance Sampling June 2 2009
- importance Sampling and Markov Chain Monte Carlo (MCMC)
- importance Sampling and Monte Carlo Simulation
- incremental Learning, Clustering and Hierarchy Formation of Whole Body Motion Patterns using Adaptive Hidden Markov Chains(Summary)
- independent Component Analysis: algorithms and applications
- is Multinomial PCA Multi-faceted Clustering or Dimensionality Reduction
- kernel Dimension Reduction in Regression
- kernel Spectral Clustering for Community Detection in Complex Networks
- kernelized Locality-Sensitive Hashing
- kernelized Sorting
- learn what not to learn
- learning2reasoning
- learning Convolutional Feature Hierarchies for Visual Recognition
- learning Fast Approximations of Sparse Coding
- learning Spectral Clustering, With Application To Speech Separation
- learning a Nonlinear Embedding by Preserving Class Neighborhood Structure
- link to my paper
- mULTIPLE OBJECT RECOGNITION WITH VISUAL ATTENTION
- main Page
- mark Your Contribution here
- mark your contribution here
- markov Chain Definitions
- matrix Completion with Noise
- maximum-Margin Matrix Factorization
- maximum Variance Unfolding (June 2 2009)
- maximum likelihood estimation of intrinsic dimension
- meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting
- measuring Statistical Dependence with Hilbert-Schmidt Norm
- measuring and testing dependence by correlation of distances
- monte Carlo Integration
- monte Carlo methods
- neighbourhood Components Analysis
- neural Machine Translation: Jointly Learning to Align and Translate
- nonlinear Dimensionality Reduction by Semidefinite Programming and Kernel Matrix Factorization
- nonparametric Latent Feature Models for Link Prediction
- on using very large target vocabulary for neural machine translation
- orthogonal gradient descent for continual learning
- overfeat: integrated recognition, localization and detection using convolutional networks
- paper 13
- paper Summaries
- policy optimization with demonstrations
- proof
- proof of Lemma 1
- proof of Theorem 1
- proposal Fall 2010
- proposal for STAT946 (Deep Learning) final projects Fall 2015
- proposal for STAT946 projects
- proposal for STAT946 projects Fall 2010
- quantifying cancer progression with conjunctive Bayesian networks.
- rOBPCA: A New Approach to Robust Principal Component Analysis
- regression on Manifold using Kernel Dimension Reduction
- relevant Component Analysis
- residual Component Analysis: Generalizing PCA for more flexible inference in linear-Gaussian models
- s13Stat946proposal
- sandbox to test w2l
- scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers Machines
- schedule
- schedule946
- schedule of Project Presentations
- self-Taught Learning
- show, Attend and Tell: Neural Image Caption Generation with Visual Attention