NeurIPS(NIPS) 2009 论文列表Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, Vancouver, British Columbia, Canada. |
The 'tree-dependent components' of natural scenes are edge filters.
Slow Learners are Fast.
Nonparametric Bayesian Texture Learning and Synthesis.
Human Rademacher Complexity.
Canonical Time Warping for Alignment of Human Behavior.
Efficient Moments-based Permutation Tests.
Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations.
Thresholding Procedures for High Dimensional Variable Selection and Statistical Estimation.
Optimizing Multi-Class Spatio-Spectral Filters via Bayes Error Estimation for EEG Classification.
DUOL: A Double Updating Approach for Online Learning.
Anomaly Detection with Score functions based on Nearest Neighbor Graphs.
Optimal Scoring for Unsupervised Learning.
A General Projection Property for Distribution Families.
Nonlinear Learning using Local Coordinate Coding.
Analysis of SVM with Indefinite Kernels.
Sparse Metric Learning via Smooth Optimization.
Conditional Random Fields with High-Order Features for Sequence Labeling.
Hierarchical Mixture of Classification Experts Uncovers Interactions between Brain Regions.
Multi-Step Dyna Planning for Policy Evaluation and Control.
Heavy-Tailed Symmetric Stochastic Neighbor Embedding.
Heterogeneous multitask learning with joint sparsity constraints.
Dirichlet-Bernoulli Alignment: A Generative Model for Multi-Class Multi-Label Multi-Instance Corpora.
Noise Characterization, Modeling, and Reduction for In Vivo Neural Recording.
Parallel Inference for Latent Dirichlet Allocation on Graphics Processing Units.
Adaptive Regularization for Transductive Support Vector Machine.
Dual Averaging Method for Regularized Stochastic Learning and Online Optimization.
Boosting with Spatial Regularization.
Statistical Consistency of Top-k Ranking.
Learning Bregman Distance Functions and Its Application for Semi-Supervised Clustering.
Fast Graph Laplacian Regularized Kernel Learning via Semidefinite-Quadratic-Linear Programming.
Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization.
Sparse Estimation Using General Likelihoods and Non-Factorial Priors.
A Neural Implementation of the Kalman Filter.
Sequential effects reflect parallel learning of multiple environmental regularities.
Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference.
Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise.
Strategy Grafting in Extensive Games.
Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation.
Sufficient Conditions for Agnostic Active Learnable.
Decoupling Sparsity and Smoothness in the Discrete Hierarchical Dirichlet Process.
A Rate Distortion Approach for Semi-Supervised Conditional Random Fields.
Variational Inference for the Nested Chinese Restaurant Process.
Rethinking LDA: Why Priors Matter.
Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model.
Tracking Dynamic Sources of Malicious Activity at Internet Scale.
Bootstrapping from Game Tree Search.
Structured output regression for detection with partial truncation.
Measuring model complexity with the prior predictive.
Gaussian process regression with Student-t likelihood.
Learning to Rank by Optimizing NDCG Measure.
Help or Hinder: Bayesian Models of Social Goal Inference.
Maximin affinity learning of image segmentation.
Compositionality of optimal control laws.
Nonlinear directed acyclic structure learning with weakly additive noise models.
Indian Buffet Processes with Power-law Behavior.
Adapting to the Shifting Intent of Search Queries.
Modelling Relational Data using Bayesian Clustered Tensor Factorization.
Efficient Recovery of Jointly Sparse Vectors.
Entropic Graph Regularization in Non-Parametric Semi-Supervised Classification.
Online Learning of Assignments.
The Wisdom of Crowds in the Recollection of Order Information.
Structural inference affects depth perception in the context of potential occlusion.
Fast Learning from Non-i.i.d. Observations.
On the Convergence of the Concave-Convex Procedure.
Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions.
Code-specific policy gradient rules for spiking neurons.
Time-Varying Dynamic Bayesian Networks.
Kernels and learning curves for Gaussian process regression on random graphs.
A Bayesian Analysis of Dynamics in Free Recall.
A Sparse Non-Parametric Approach for Single Channel Separation of Known Sounds.
Hierarchical Modeling of Local Image Features through $L_p$-Nested Symmetric Distributions.
Semi-supervised Learning using Sparse Eigenfunction Bases.
Learning Label Embeddings for Nearest-Neighbor Multi-class Classification with an Application to Speech Recognition.
Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling.
Fast subtree kernels on graphs.
Positive Semidefinite Metric Learning with Boosting.
Improving Existing Fault Recovery Policies.
Speeding up Magnetic Resonance Image Acquisition by Bayesian Multi-Slice Adaptive Compressed Sensing.
Linearly constrained Bayesian matrix factorization for blind source separation.
Learning models of object structure.
Learning in Markov Random Fields using Tempered Transitions.
Replicated Softmax: an Undirected Topic Model.
Filtering Abstract Senses From Image Search Results.
Segmenting Scenes by Matching Image Composites.
Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness.
Spatial Normalized Gamma Processes.
Asymptotic Analysis of MAP Estimation via the Replica Method and Compressed Sensing.
Linear-time Algorithms for Pairwise Statistical Problems.
Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions.
Multi-Label Prediction via Sparse Infinite CCA.
Locality-sensitive binary codes from shift-invariant kernels.
Convex Relaxation of Mixture Regression with Efficient Algorithms.
Distribution Matching for Transduction.
Bilinear classifiers for visual recognition.
Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models.
Know Thy Neighbour: A Normative Theory of Synaptic Depression.
Exponential Family Graph Matching and Ranking.
Robust Value Function Approximation Using Bilinear Programming.
Maximum likelihood trajectories for continuous-time Markov chains.
Free energy score space.
Conditional Neural Fields.
Zero-shot Learning with Semantic Output Codes.
Submanifold density estimation.
Learning from Neighboring Strokes: Combining Appearance and Context for Multi-Domain Sketch Recognition.
Construction of Nonparametric Bayesian Models from Parametric Bayes Equations.
Correlation Coefficients are Insufficient for Analyzing Spike Count Dependencies.
Noisy Generalized Binary Search.
STDP enables spiking neurons to detect hidden causes of their inputs.
A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers.
3D Object Recognition with Deep Belief Nets.
Statistical Analysis of Semi-Supervised Learning: The Limit of Infinite Unlabelled Data.
Predicting the Optimal Spacing of Study: A Multiscale Context Model of Memory.
A Generalized Natural Actor-Critic Algorithm.
Which graphical models are difficult to learn?
Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process.
Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models.
Nonparametric Latent Feature Models for Link Prediction.
Extending Phase Mechanism to Differential Motion Opponency for Motion Pop-out.
Matrix Completion from Power-Law Distributed Samples.
FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs.
Toward Provably Correct Feature Selection in Arbitrary Domains.
Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models.
Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships.
Compressed Least-Squares Regression.
Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation.
Bayesian estimation of orientation preference maps.
Occlusive Components Analysis.
Variational Gaussian-process factor analysis for modeling spatio-temporal data.
Who's Doing What: Joint Modeling of Names and Verbs for Simultaneous Face and Pose Annotation.
Modeling the spacing effect in sequential category learning.
Grouped Orthogonal Matching Pursuit for Variable Selection and Prediction.
Nonparametric Greedy Algorithms for the Sparse Learning Problem.
Asymptotically Optimal Regularization in Smooth Parametric Models.
Probabilistic Relational PCA.
An Integer Projected Fixed Point Method for Graph Matching and MAP Inference.
Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning.
Unsupervised feature learning for audio classification using convolutional deep belief networks.
Inter-domain Gaussian Processes for Sparse Inference using Inducing Features.
Monte Carlo Sampling for Regret Minimization in Extensive Games.
Ensemble Nystrom Method.
Learning a Small Mixture of Trees.
Learning to Hash with Binary Reconstructive Embeddings.
Fast Image Deconvolution using Hyper-Laplacian Priors.
Fast, smooth and adaptive regression in metric spaces.
Skill Discovery in Continuous Reinforcement Learning Domains using Skill Chaining.
Sparsistent Learning of Varying-coefficient Models with Structural Changes.
Efficient and Accurate Lp-Norm Multiple Kernel Learning.
Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks.
Clustering sequence sets for motif discovery.
Unsupervised Detection of Regions of Interest Using Iterative Link Analysis.
Semi-supervised Regression using Hessian energy with an application to semi-supervised dimensionality reduction.
Matrix Completion from Noisy Entries.
Quantification and the language of thought.
Abstraction and Relational learning.
Individuation, Identification and Object Discovery.
Submodularity Cuts and Applications.
Multiple Incremental Decremental Learning of Support Vector Machines.
Breaking Boundaries Between Induction Time and Diagnosis Time Active Information Acquisition.
Directed Regression.
Potential-Based Agnostic Boosting.
Local Rules for Global MAP: When Do They Work ?
Regularized Distance Metric Learning: Theory and Algorithm.
Bayesian Belief Polarization.
On the Algorithmics and Applications of a Mixed-norm based Kernel Learning Formulation.
Jagarlapudi Saketha Nath G. Dinesh Raman Sankaran Chiranjib Bhattacharyya Aharon Ben-Tal K. R. Ramakrishnan
Modeling Social Annotation Data with Content Relevance using a Topic Model.
Particle-based Variational Inference for Continuous Systems.
Discrete MDL Predicts in Total Variation.
Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data.
Riffled Independence for Ranked Data.
Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME).
Accelerated Gradient Methods for Stochastic Optimization and Online Learning.
Differential Use of Implicit Negative Evidence in Generative and Discriminative Language Learning.
Multi-Label Prediction via Compressed Sensing.
Periodic Step Size Adaptation for Single Pass On-line Learning.
Sparse and Locally Constant Gaussian Graphical Models.
Bayesian Sparse Factor Models and DAGs Inference and Comparison.
Hierarchical Learning of Dimensional Biases in Human Categorization.
Robust Nonparametric Regression with Metric-Space Valued Output.
On Stochastic and Worst-case Models for Investing.
Beyond Convexity: Online Submodular Minimization.
Label Selection on Graphs.
Non-stationary continuous dynamic Bayesian networks.
A Fast, Consistent Kernel Two-Sample Test.
Posterior vs Parameter Sparsity in Latent Variable Models.
Region-based Segmentation and Object Detection.
Measuring Invariances in Deep Networks.
A Gaussian Tree Approximation for Integer Least-Squares.
A Biologically Plausible Model for Rapid Natural Scene Identification.
A joint maximum-entropy model for binary neural population patterns and continuous signals.
Bayesian Source Localization with the Multivariate Laplace Prior.
Perceptual Multistability as Markov Chain Monte Carlo Inference.
From PAC-Bayes Bounds to KL Regularization.
Lattice Regression.
Graph-based Consensus Maximization among Multiple Supervised and Unsupervised Models.
Estimating image bases for visual image reconstruction from human brain activity.
An LP View of the M-best MAP problem.
An Additive Latent Feature Model for Transparent Object Recognition.
Sharing Features among Dynamical Systems with Beta Processes.
Orthogonal Matching Pursuit From Noisy Random Measurements: A New Analysis.
Evaluating multi-class learning strategies in a generative hierarchical framework for object detection.
Semi-Supervised Learning in Gigantic Image Collections.
Subject independent EEG-based BCI decoding.
A Data-Driven Approach to Modeling Choice.
Streaming Pointwise Mutual Information.
Efficient Learning using Forward-Backward Splitting.
A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation.
The Infinite Partially Observable Markov Decision Process.
Localizing Bugs in Program Executions with Graphical Models.
A Smoothed Approximate Linear Program.
Solving Stochastic Games.
Distribution-Calibrated Hierarchical Classification.
L1-Penalized Robust Estimation for a Class of Inverse Problems Arising in Multiview Geometry.
White Functionals for Anomaly Detection in Dynamical Systems.
Learning transport operators for image manifolds.
Adaptive Regularization of Weight Vectors.
An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism.
Learning Non-Linear Combinations of Kernels.
Sensitivity analysis in HMMs with application to likelihood maximization.
fMRI-Based Inter-Subject Cortical Alignment Using Functional Connectivity.
Statistical Models of Linear and Nonlinear Contextual Interactions in Early Visual Processing.
AUC optimization and the two-sample problem.
Approximating MAP by Compensating for Structural Relaxations.
Kernel Methods for Deep Learning.
The Ordered Residual Kernel for Robust Motion Subspace Clustering.
Ranking Measures and Loss Functions in Learning to Rank.
Factor Modeling for Advertisement Targeting.
An Online Algorithm for Large Scale Image Similarity Learning.
A Parameter-free Hedging Algorithm.
Reading Tea Leaves: How Humans Interpret Topic Models.
Exploring Functional Connectivities of the Human Brain using Multivariate Information Analysis.
Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes.
Learning with Compressible Priors.
Discriminative Network Models of Schizophrenia.
Guillermo A. Cecchi Irina Rish Benjamin Thyreau Bertrand Thirion Marion Plaze Marie-Laure Paillère-Martinot Catherine Martelli Jean-Luc Martinot Jean-Baptiste Poline
Efficient Bregman Range Search.
Adaptive Design Optimization in Experiments with People.
Bayesian Nonparametric Models on Decomposable Graphs.
A Stochastic approximation method for inference in probabilistic graphical models.
Speaker Comparison with Inner Product Discriminant Functions.
Learning to Explore and Exploit in POMDPs.
Manifold Embeddings for Model-Based Reinforcement Learning under Partial Observability.
A Game-Theoretic Approach to Hypergraph Clustering.
Nash Equilibria of Static Prediction Games.
Optimal context separation of spiking haptic signals by second-order somatosensory neurons.
On Invariance in Hierarchical Models.
Unsupervised Feature Selection for the $k$-means Clustering Problem.
Randomized Pruning: Efficiently Calculating Expectations in Large Dynamic Programs.
Efficient Match Kernel between Sets of Features for Visual Recognition.
Augmenting Feature-driven fMRI Analyses: Semi-supervised learning and resting state activity.
Manifold Regularization for SIR with Rate Root-n Convergence.
No evidence for active sparsification in the visual cortex.
Slow, Decorrelated Features for Pretraining Complex Cell-like Networks.
Neurometric function analysis of population codes.
Group Sparse Coding.
Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution.
Polynomial Semantic Indexing.
Bing Bai Jason Weston David Grangier Ronan Collobert Kunihiko Sadamasa Yanjun Qi Corinna Cortes Mehryar Mohri
On Learning Rotations.
Data-driven calibration of linear estimators with minimal penalties.
Constructing Topological Maps using Markov Random Fields and Loop-Closure Detection.
Learning from Multiple Partially Observed Views - an Application to Multilingual Text Categorization.
Complexity of Decentralized Control: Special Cases.
Streaming k-means approximation.
Information-theoretic lower bounds on the oracle complexity of convex optimization.