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- Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
- Universal Style Transfer via Feature Transforms
- Unsupervised Domain Adaptation with Residual Transfer Networks
- Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness
- Unsupervised Machine Translation Using Monolingual Corpora Only
- Unsupervised Neural Machine Translation
- Very Deep Convoloutional Networks for Large-Scale Image Recognition
- Video-Based Face Recognition Using Adaptive Hidden Markov Models
- Video-based face recognition using Adaptive HMM
- Visual Reinforcement Learning with Imagined Goals
- Visualizing Data using t-SNE
- Visualizing Similarity Data with a Mixture of Maps
- Wasserstein Auto-Encoders
- Wasserstein Auto-encoders
- Wavelet Pooling CNN
- When Does Self-Supervision Improve Few-Shot Learning?
- When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, l2-consistency and Neuroscience Applications: Summary
- Wide and Deep Learning for Recommender Systems
- Wikicoursenote:Manual of Style
- Wikicoursenote:cleanup
- Word translation without parallel data
- XGBoost
- XGBoost: A Scalable Tree Boosting System
- Zero-Shot Visual Imitation
- a Deeper Look into Importance Sampling
- a Direct Formulation For Sparse PCA Using Semidefinite Programming
- a Dynamic Bayesian Network Click Model for Web Search Ranking
- a Dynamic Bayesian Network Click Model for web search ranking
- a New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
- a Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis
- 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
- acceptance-Rejection Sampling
- adaptive dimension reduction for clustering high dimensional data
- again on Markov Chain
- an HDP-HMM for Systems with State Persistence
- bayesian and Frequentist Schools of Thought
- binomial Probability Monte Carlo Sampling June 2 2009
- cardinality Restricted Boltzmann Machines
- cm361
- compressed Sensing Reconstruction via Belief Propagation
- compressive Sensing
- compressive Sensing (Candes)
- conditional neural process
- consistency of Trace Norm Minimization
- context Adaptive Training with Factorized Decision Trees for HMM-Based Speech Synthesis
- continuous space language models
- 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
- convex and Semi Nonnegative Matrix Factorization
- copyofstat341
- decentralised Data Fusion: A Graphical Model Approach (Summary)
- deepGenerativeModels
- deep Convolutional Neural Networks For LVCSR
- deep Generative Stochastic Networks Trainable by Backprop
- deep Learning of the tissue-regulated splicing code
- deep Neural Nets as a Method for Quantitative Structure–Activity Relationships
- deep Sparse Rectifier Neural Networks
- 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
- dropout
- extracting and Composing Robust Features with Denoising Autoencoders
- f10 Stat841 digest
- f11Stat841EditorSignUp
- f11Stat841presentation
- f11Stat841proposal
- f11Stat946ass
- f11Stat946papers
- f11Stat946presentation
- f11stat946EditorSignUp
- f14Stat842EditorSignUp
- f15Stat946PaperSignUp
- f17Stat946PaperSignUp
- from Machine Learning to Machine Reasoning
- generating Random Numbers
- generating text with recurrent neural networks
- genetics
- goingDeeperWithConvolutions
- graph Laplacian Regularization for Larg-Scale Semidefinite Programming
- graphical models for structured classification, with an application to interpreting images of protein subcellular location patterns
- graves et al., Speech recognition with deep recurrent neural networks
- hamming Distance Metric Learning
- hierarchical Dirichlet Processes
- human-level control through deep reinforcement learning
- imageNet Classification with Deep Convolutional Neural Networks
- 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
- inductive Kernel Low-rank Decomposition with Priors: A Generalized Nystrom Method
- infoboxtest
- is Multinomial PCA Multi-faceted Clustering or Dimensionality Reduction
- joint training of a convolutional network and a graphical model for human pose estimation
- kernel Dimension Reduction in Regression
- kernel Spectral Clustering for Community Detection in Complex Networks
- kernelized Locality-Sensitive Hashing
- kernelized Sorting
- large-Scale Supervised Sparse Principal Component Analysis
- learn what not to learn
- learning2reasoning
- learning Convolutional Feature Hierarchies for Visual Recognition
- learning Fast Approximations of Sparse Coding
- learning Hierarchical Features for Scene Labeling
- learning Long-Range Vision for Autonomous Off-Road Driving
- learning Phrase Representations
- 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
- markov Random Fields for Super-Resolution
- 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
- measuring statistical dependence with Hilbert-Schmidt norms
- memory Networks
- metric and Kernel Learning Using a Linear Transformation
- monte Carlo Integration
- monte Carlo methods
- multi-Task Feature Learning
- natural language processing (almost) from scratch.
- neighbourhood Components Analysis
- neural Machine Translation: Jointly Learning to Align and Translate
- neural Turing Machines
- neural sketch drawings
- new page
- nonlinear Dimensionality Reduction by Semidefinite Programming and Kernel Matrix Factorization
- nonparametric Latent Feature Models for Link Prediction
- on the Number of Linear Regions of Deep Neural Networks
- on the difficulty of training recurrent neural networks
- on using very large target vocabulary for neural machine translation
- optimal Solutions forSparse Principal Component Analysis
- orthogonal gradient descent for continual learning
- overfeat: integrated recognition, localization and detection using convolutional networks
- paper 13
- paper Summaries
- parametric Local Metric Learning for Nearest Neighbor Classification
- parsing natural scenes and natural language with recursive neural networks
- policy optimization with demonstrations
- positive Semidefinite Metric Learning Using Boosting-like Algorithms
- probabilistic Matrix Factorization
- probabilistic PCA with GPLVM
- 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
- quantifying cancer progression with conjunctive Bayesian networks.
- question Answering with Subgraph Embeddings
- rOBPCA: A New Approach to Robust Principal Component Analysis
- regression on Manifold using Kernel Dimension Reduction
- regression on Manifolds 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
- semi-supervised Learning with Deep Generative Models
- show, Attend and Tell: Neural Image Caption Generation with Visual Attention
- sign up for your presentation
- signupformStat341F11
- singular Value Decomposition(SVD)
- sparse PCA
- stat340s13
- stat341
- stat341 / CM 361
- stat341f11
- stat441F18
- stat441F18/TCNLM
- stat441F18/YOLO
- stat441F20
- stat441F21
- stat441w18
- stat441w18/A New Method of Region Embedding for Text Classification
- stat441w18/Convolutional Neural Networks for Sentence Classification
- stat441w18/Image Question Answering using CNN with Dynamic Parameter Prediction
- stat441w18/Saliency-based Sequential Image Attention with Multiset Prediction
- stat441w18/e-gan
- stat441w18/mastering-chess-and-shogi-self-play
- stat441w18/summary 1
- stat841
- stat841F18/
- stat841f10
- stat841f11
- stat841f14
- stat940F21
- stat946
- stat946-Fall 2010
- stat946F18
- stat946F18/Autoregressive Convolutional Neural Networks for Asynchronous Time Series
- stat946F18/Beyond Word Importance Contextual Decomposition to Extract Interactions from LSTMs
- stat946F18/Hierarchical Representations for Efficient Architecture Search
- stat946F18/differentiableplasticity
- stat946F20
- stat946F20/GradientLess Descent
- stat946f10
- stat946f11
- stat946f11pool
- stat946f15
- stat946f15/Deep neural networks for acoustic modeling in speech recognition
- stat946f15/Sequence to sequence learning with neural networks
- stat946f17
- stat946s13
- stat946w18
- stat946w18/
- stat946w18/AmbientGAN: Generative Models from Lossy Measurements
- stat946w18/Hierarchical Representations for Efficient Architecture Search
- stat946w18/IMPROVING GANS USING OPTIMAL TRANSPORT
- stat946w18/Implicit Causal Models for Genome-wide Association Studies
- stat946w18/MaskRNN: Instance Level Video Object Segmentation
- stat946w18/Predicting Floor-Level for 911 Calls with Neural Networks and Smartphone Sensor Data
- stat946w18/Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolutional Layers
- stat946w18/Self Normalizing Neural Networks
- stat946w18/Spectral
- stat946w18/Spectral normalization for generative adversial network
- stat946w18/Synthetic and natural noise both break neural machine translation
- stat946w18/Tensorized LSTMs
- stat946w18/Towards Image Understanding From Deep Compression Without Decoding
- stat946w18/Unsupervised Machine Translation Using Monolingual Corpora Only
- stat946w18/Wavelet Pooling For Convolutional Neural Networks
- statf09841Proposal
- statf09841Scribe
- statf10841Scribe
- strategies for Training Large Scale Neural Network Language Models
- summary
- supervised Dictionary Learning
- tRIAL for that odd behaviour
- techniques for Normal and Gamma Sampling
- test
- test1
- the Indian Buffet Process: An Introduction and Review
- the Manifold Tangent Classifier
- the Wake-Sleep Algorithm for Unsupervised Neural Networks
- the loss surfaces of multilayer networks (Choromanska et al.)
- time-series-using-GAN
- uncovering Shared Structures in Multiclass Classification
- understanding image motion with group representations
- very Deep Convoloutional Networks for Large-Scale Image Recognition
- video-Based Face Recognition Using Adaptive Hidden Markov Models
- video-based face recognition using Adaptive HMM
- visualizing Data using t-SNE
- visualizing Similarity Data with a Mixture of Maps
- what game are we playing
- wikicoursenote:Manual of Style
- wikicoursenote:cleanup