Downloads 2009
Number of events: 320
- L1-Penalized Robust Estimation for a Class of Inverse Problems Arising in Multiview Geometry
- 3D Object Recognition with Deep Belief Nets
- A Bayesian Analysis of Dynamics in Free Recall
- A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation
- A Biologically Plausible Model for Rapid Natural Scene Identification
- Abstraction and Relational learning
- Accelerated Gradient Methods for Stochastic Optimization and Online Learning
- Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models
- Activity Recognition and Human Gait Recognition
- Adapting to the Shifting Intent of Search Queries
- Adaptive Design Optimization in Experiments with People
- Adaptive Regularization for Transductive Support Vector Machine
- Adaptive Regularization of Weight Vectors
- Adaptive Sensing, Active Learning, and Experimental Design
- A Data-Driven Approach to Modeling Choice
- Advances in Ranking
- A Fast, Consistent Kernel Two-Sample Test
- A Game-Theoretic Approach to Hypergraph Clustering
- A Gaussian Tree Approximation for Integer Least-Squares
- A Generalized Natural Actor-Critic Algorithm
- A General Projection Property for Distribution Families
- A joint maximum-entropy model for binary neural population patterns and continuous signals
- An Additive Latent Feature Model for Transparent Object Recognition
- Analysis and Design of Algorithms for Interactive Machine Learning
- Analysis of SVM with Indefinite Kernels
- Analyzing Networks and Learning With Graphs
- An Efficient Interior-Point Method for Minimum-Regret Learning in Online Convex Optimization
- A Neural Implementation of the Kalman Filter
- An FPGA-based Multi-Algorithm Learning and Classification Engine
- An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism
- An Integer Projected Fixed Point Method for Graph Matching and MAP Inference
- An LP View of the M-best MAP problem
- Anomaly Detection with Score functions based on Nearest Neighbor Graphs
- An Online Algorithm for Large Scale Image Similarity Learning
- A Parameter-free Hedging Algorithm
- Applications for Topic Models: Text and Beyond
- Approximate Learning of Large Scale Graphical Models
- Approximating MAP by Compensating for Structural Relaxations
- A Rate Distortion Approach for Semi-Supervised Conditional Random Fields
- A Smoothed Approximate Linear Program
- A Sparse Non-Parametric Approach for Single Channel Separation of Known Sounds
- A Stochastic approximation method for inference in probabilistic graphical models
- Asymptotically Optimal Regularization in Smooth Parametric Models
- Asymptotic Analysis of MAP Estimation via the Replica Method and Compressed Sensing
- AUC optimization and the two-sample problem
- Augmenting Feature-driven fMRI Analyses: Semi-supervised learning and resting state activity
- A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers
- Automatically Adaptable Spike Sorting Software
- Bayesian Analysis of Markov Chains
- Bayesian Belief Polarization
- Bayesian estimation of orientation preference maps
- Bayesian Nonparametric Models on Decomposable Graphs
- Bayesian Source Localization with the Multivariate Laplace Prior
- Bayesian Sparse Factor Models and DAGs Inference and Comparison
- Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships
- Beyond Convexity: Online Submodular Minimization
- Bilinear classifiers for visual recognition
- Boosting with Spatial Regularization
- Bootstrapping from Game Tree Search
- Bounded-rational analyses of human cognition: Bayesian models, approximate inference, and the brain
- Breaking Boundaries Between Induction Time and Diagnosis Time Active Information Acquisition
- Canonical Time Warping for Alignment of Human Behavior
- Car Detector trained with the Quantum Adiabatic Algorithm
- Causality Workbench
- Clustering: Science or art? Towards principled approaches
- Clustering sequence sets for motif discovery
- Code-specific policy gradient rules for spiking neurons
- Cognitive Dynamic Radio
- Complexity of Decentralized Control: Special Cases
- Compositionality of optimal control laws
- Compressed Least-Squares Regression
- Conditional Neural Fields
- Conditional Random Fields with High-Order Features for Sequence Labeling
- Connectivity Inference in Neuroimaging
- Constructing Topological Maps using Markov Random Fields and Loop-Closure Detection
- Construction of Nonparametric Bayesian Models from Parametric Bayes Equations
- Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation
- Convex Relaxation of Mixture Regression with Efficient Algorithms
- Correlation Coefficients are Insufficient for Analyzing Spike Count Dependencies
- Curious George: An Attentive Semantic Robotic Vision System
- Data-driven calibration of linear estimators with minimal penalties
- Decoupling Sparsity and Smoothness in the Discrete Hierarchical Dirichlet Process
- Deep Learning for Speech Recognition and Related Applications
- Deep Learning in Natural Language Processing
- Deep Learning with Multiplicative Interactions
- Deformed Lattice Detection in Real-World Images Using a Novel Mean-Shift Belief
- Demonstration of the TreeRank Software
- Demonstrations
- Demonstrations
- Differential Use of Implicit Negative Evidence in Generative and Discriminative Language Learning
- Directed Regression
- Dirichlet-Bernoulli Alignment: A Generative Model for Multi-Class Multi-Label Multi-Instance Corpora
- Discrete MDL Predicts in Total Variation
- Discrete Optimization in Machine Learning: Submodularity, Polyhedra and Sparsity
- Discriminative Network Models of Schizophrenia
- Distribution-Calibrated Hierarchical Classification
- Distribution Matching for Transduction
- Dual Averaging Method for Regularized Stochastic Learning and Online Optimization
- DUOL: A Double Updating Approach for Online Learning
- Efficient and Accurate Lp-Norm Multiple Kernel Learning
- Efficient Bregman Range Search
- Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models
- Efficient Learning using Forward-Backward Splitting
- Efficient Match Kernel between Sets of Features for Visual Recognition
- Efficient Moments-based Permutation Tests
- Efficient Recovery of Jointly Sparse Vectors
- Ensemble Nystrom Method
- Entropic Graph Regularization in Non-Parametric Semi-Supervised Classification
- Estimating image bases for visual image reconstruction from human brain activity
- Evaluating multi-class learning strategies in a generative hierarchical framework for object detection
- Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model
- Exploring Functional Connectivities of the Human Brain using Multivariate Information Analysis
- Exponential Family Graph Matching and Ranking
- Extending Phase Mechanism to Differential Motion Opponency for Motion Pop-out
- FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs
- Factor Modeling for Advertisement Targeting
- Fast Graph Laplacian Regularized Kernel Learning via Semidefinite–Quadratic–Linear Programming
- Fast Image Deconvolution using Hyper-Laplacian Priors
- Fast Learning from Non-i.i.d. Observations
- Fast, smooth and adaptive regression in metric spaces
- Fast subtree kernels on graphs
- Filtering Abstract Senses From Image Search Results
- fMRI-Based Inter-Subject Cortical Alignment Using Functional Connectivity
- Free energy score space
- From PAC-Bayes Bounds to KL Regularization
- Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning
- Gaussian process regression with Student-t likelihood
- Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes
- Grammar Induction, Representation of Language and Language Learning
- Graph-based Consensus Maximization among Multiple Supervised and Unsupervised Models
- Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation
- Grouped Orthogonal Matching Pursuit for Variable Selection and Prediction
- Group Sparse Coding
- Heavy-Tailed Symmetric Stochastic Neighbor Embedding
- Help or Hinder: Bayesian Models of Social Goal Inference
- Heterogeneous multitask learning with joint sparsity constraints
- Hierarchical Learning of Dimensional Biases in Human Categorization
- Hierarchical Mixture of Classification Experts Uncovers Interactions between Brain Regions
- Hierarchical Modeling of Local Image Features through Lp-Nested Symmetric Distributions
- Human Rademacher Complexity
- Improving Existing Fault Recovery Policies
- Indian Buffet Processes with Power-law Behavior
- Individuation, Identification and Object Discovery
- Information-theoretic lower bounds on the oracle complexity of convex optimization
- Inter-domain Gaussian Processes for Sparse Inference using Inducing Features
- Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions
- Kernel Methods for Deep Learning
- Kernels and learning curves for Gaussian process regression on random graphs
- Kernels for Multiple Outputs and Multi-task Learning: Frequentist and Bayesian Points of View
- Know Thy Neighbour: A Normative Theory of Synaptic Depression
- Label Selection on Graphs
- Large-Scale Machine Learning: Parallelism and Massive Datasets
- Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process
- Lattice Regression
- Learning and Inference in Low-Level Vision
- Learning a Small Mixture of Trees
- Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data
- Learning Bregman Distance Functions and Its Application for Semi-Supervised Clustering
- Learning from Multiple Partially Observed Views - an Application to Multilingual Text Categorization
- Learning from Multiple Sources with Applications to Robotics
- Learning from Neighboring Strokes: Combining Appearance and Context for Multi-Domain Sketch Recognition
- Learning in Markov Random Fields using Tempered Transitions
- Learning Label Embeddings for Nearest-Neighbor Multi-class Classification with an Application to Speech Recognition
- Learning models of object structure
- Learning Non-Linear Combinations of Kernels
- Learning to Explore and Exploit in POMDPs
- Learning to Hash with Binary Reconstructive Embeddings
- Learning to Rank by Optimizing NDCG Measure
- Learning transport operators for image manifolds
- Learning with Compressible Priors
- Learning with Orderings
- Linearly constrained Bayesian matrix factorization for blind source separation
- Linear-time Algorithms for Pairwise Statistical Problems
- Locality-sensitive binary codes from shift-invariant kernels
- Localizing Bugs in Program Executions with Graphical Models
- Local Rules for Global MAP: When Do They Work ?
- Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness
- Machine Learning in Computational Biology
- Making Very Large-Scale Linear Algebraic Computations Possible Via Randomization
- Manifold Embeddings for Model-Based Reinforcement Learning under Partial Observability
- Manifold Regularization for SIR with Rate Root-n Convergence
- Manifolds, sparsity, and structured models: When can low-dimensional geometry really help?
- Matrix Completion from Noisy Entries
- Matrix Completion from Power-Law Distributed Samples
- Maximin affinity learning of image segmentation
- Maximum likelihood trajectories for continuous-time Markov chains
- Measuring Invariances in Deep Networks
- Measuring model complexity with the prior predictive
- Model-Based Reinforcement Learning
- Modeling Social Annotation Data with Content Relevance using a Topic Model
- Modeling the spacing effect in sequential category learning
- Modelling Relational Data using Bayesian Clustered Tensor Factorization
- Monte: An Interactive Ssytem for Massively Parallel Probabilistic Programming
- Monte Carlo Sampling for Regret Minimization in Extensive Games
- Multi-Label Prediction via Compressed Sensing
- Multi-Label Prediction via Sparse Infinite CCA
- Multiple Incremental Decremental Learning of Support Vector Machines
- Multi-Step Dyna Planning for Policy Evaluation and Control
- Nash Equilibria of Static Prediction Games
- Nengo: Large-Scale Integrated Neural Modeling
- Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling
- Neurometric function analysis of population codes
- No evidence for active sparsification in the visual cortex
- Noise Characterization, Modeling, and Reduction for In Vivo Neural Recording
- Noisy Generalized Binary Search
- Nonlinear directed acyclic structure learning with weakly additive noise models
- Nonlinear Learning using Local Coordinate Coding
- Nonparametric Bayes
- Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations
- Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution
- Nonparametric Bayesian Texture Learning and Synthesis
- Nonparametric Greedy Algorithms for the Sparse Learning Problem
- Nonparametric Latent Feature Models for Link Prediction
- Non-stationary continuous dynamic Bayesian networks
- Normative electrophysiology: Explaining cellular properties of neurons from first principles
- Occlusive Components Analysis
- On Invariance in Hierarchical Models
- On Learning Rotations
- Online Learning of Assignments
- On Stochastic and Worst-case Models for Investing
- On the Algorithmics and Applications of a Mixed-norm based Kernel Learning Formulation
- On the Convergence of the Concave-Convex Procedure
- Optimal context separation of spiking haptic signals by second-order somatosensory neurons
- Optimal Scoring for Unsupervised Learning
- Optimization for Machine Learning
- Optimizing Multi-Class Spatio-Spectral Filters via Bayes Error Estimation for EEG Classification
- Optogenetics: Development and Application
- Orthogonal Matching Pursuit From Noisy Random Measurements: A New Analysis
- Parallel Inference for Latent Dirichlet Allocation on Graphics Processing Units
- Particle-based Variational Inference for Continuous Systems
- Perceptual Multistability as Markov Chain Monte Carlo Inference
- Periodic Step Size Adaptation for Single Pass On-line Learning
- Polynomial Semantic Indexing
- Positive Semidefinite Metric Learning with Boosting
- Posterior vs Parameter Sparsity in Latent Variable Models
- Potential-Based Agnostic Boosting
- Predicting the Optimal Spacing of Study: A Multiscale Context Model of Memory
- Probabilistic Approaches for Control and Robotics
- Probabilistic Relational PCA
- Quantification and the language of thought
- Randomized Pruning: Efficiently Calculating Expectations in Large Dynamic Programs
- Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions
- Ranking Measures and Loss Functions in Learning to Rank
- Reading Tea Leaves: How Humans Interpret Topic Models
- Real-Time Adaptive Face Recognition
- Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME)
- Region-based Segmentation and Object Detection
- Regularized Distance Metric Learning:Theory and Algorithm
- Relative Entropy
- Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks
- Replicated Softmax: an Undirected Topic Model
- Rethinking LDA: Why Priors Matter
- Riffled Independence for Ranked Data
- Robust Nonparametric Regression with Metric-Space Valued Output
- Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization
- Robust Speaker Recognition Using Approximate Bayesian Inference
- Robust Value Function Approximation Using Bilinear Programming
- Segmenting Scenes by Matching Image Composites
- Semi-Supervised Learning in Gigantic Image Collections
- Semi-supervised Learning using Sparse Eigenfunction Bases
- Semi-supervised Regression using Hessian energy with an application to semi-supervised dimensionality reduction
- Sensitivity analysis in HMMs with application to likelihood maximization
- Sequential effects reflect parallel learning of multiple environmental regularities
- Sequential Monte-Carlo Methods
- Sharing Features among Dynamical Systems with Beta Processes
- Skill Discovery in Continuous Reinforcement Learning Domains using Skill Chaining
- Slow, Decorrelated Features for Pretraining Complex Cell-like Networks
- Slow Learners are Fast
- Solving Stochastic Games
- Sparse and Locally Constant Gaussian Graphical Models
- Sparse Estimation Using General Likelihoods and Non-Factorial Priors
- Sparse Methods for Machine Learning: Theory and Algorithms
- Sparse Metric Learning via Smooth Optimization
- Sparsistent Learning of Varying-coefficient Models with Structural Changes
- Spatial Normalized Gamma Processes
- Speaker Comparison with Inner Product Discriminant Functions
- Speeding up Magnetic Resonance Image Acquisition by Bayesian Multi-Slice Adaptive Compressed Sensing
- Statistical Analysis of Semi-Supervised Learning: The Limit of Infinite Unlabelled Data
- Statistical Consistency of Top-k Ranking
- Statistical Machine Learning for Visual Analytics
- Statistical Models of Linear and Nonlinear Contextual Interactions in Early Visual Processing
- STDP enables spiking neurons to detect hidden causes of their inputs
- Strategy Grafting in Extensive Games
- Streaming k-means approximation
- Streaming Pointwise Mutual Information
- Structural inference affects depth perception in the context of potential occlusion
- Structured output regression for detection with partial truncation
- Subject independent EEG-based BCI decoding
- Submanifold density estimation
- Submodularity Cuts and Applications
- Sufficient Conditions for Agnostic Active Learnable
- Temporal Segmentation: Perspectives from Statistics, Machine Learning, and Signal Processing
- The Curse of Dimensionality Problem: How Can the Brain Solve It?
- The Generative and Discriminative Learning Interface
- The IID: A Natively Probabilistic Reconfigurable Computer
- The Infinite Partially Observable Markov Decision Process
- The Ordered Residual Kernel for Robust Motion Subspace Clustering
- The Rat Vibrissal Array as a Model Sensorimotor System
- The "tree-dependent components" of natural scenes are edge filters
- The Wisdom of Crowds in the Recollection of Order Information
- Thresholding Procedures for High Dimensional Variable Selection and Statistical Estimation
- Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models
- Time-Varying Dynamic Bayesian Networks
- Toward Provably Correct Feature Selection in Arbitrary Domains
- Tracking Dynamic Sources of Malicious Activity at Internet Scale
- Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference
- Transfer Learning for Structured Data
- Understanding Multiple Kernel Learning Methods
- Understanding Visual Scenes
- Unsupervised Detection of Regions of Interest Using Iterative Link Analysis
- Unsupervised feature learning for audio classification using convolutional deep belief networks
- Unsupervised Feature Selection for the k-means Clustering Problem
- Variational Gaussian-process factor analysis for modeling spatio-temporal data
- Variational Inference for the Nested Chinese Restaurant Process
- Vision Toolkit for Image Recognition
- Which graphical models are difficult to learn?
- White Functionals for Anomaly Detection in Dynamical Systems
- Who’s Doing What: Joint Modeling of Names and Verbs for Simultaneous Face and Pose Annotation
- Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise
- Zero-shot Learning with Semantic Output Codes