NeurIPS(NIPS) 2004 论文列表
Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, NIPS 2004, December 13-18, 2004, Vancouver, British Columbia, Canada].
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Learning first-order Markov models for control.
Learning Syntactic Patterns for Automatic Hypernym Discovery.
Distributed Occlusion Reasoning for Tracking with Nonparametric Belief Propagation.
Generalization Error and Algorithmic Convergence of Median Boosting.
Planning for Markov Decision Processes with Sparse Stochasticity.
An Information Maximization Model of Eye Movements.
Semi-supervised Learning with Penalized Probabilistic Clustering.
Joint Tracking of Pose, Expression, and Texture using Conditionally Gaussian Filters.
Identifying Protein-Protein Interaction Sites on a Genome-Wide Scale.
Distributed Information Regularization on Graphs.
A Machine Learning Approach to Conjoint Analysis.
Constraining a Bayesian Model of Human Visual Speed Perception.
Using Machine Learning to Break Visual Human Interaction Proofs (HIPs).
Temporal-Difference Networks.
Binet-Cauchy Kernels.
Multiple Relational Embedding.
Maximum Margin Clustering.
Learning Efficient Auditory Codes Using Spikes Predicts Cochlear Filters.
Nearly Tight Bounds for the Continuum-Armed Bandit Problem.
Online Bounds for Bayesian Algorithms.
L_0-norm Minimization for Basis Selection.
Multi-agent Cooperation in Diverse Population Games.
Conditional Random Fields for Object Recognition.
Kernels for Multi--task Learning.
Comparing Beliefs, Surveys, and Random Walks.
Theory of localized synfire chain: characteristic propagation speed of stable spike pattern.
Machine Learning Applied to Perception: Decision Images for Gender Classification.
Proximity Graphs for Clustering and Manifold Learning.
Experts in a Markov Decision Process.
Computing regularization paths for learning multiple kernels.
Theories of Access Consciousness.
Co-Validation: Using Model Disagreement on Unlabeled Data to Validate Classification Algorithms.
Triangle Fixing Algorithms for the Metric Nearness Problem.
Learning Gaussian Process Kernels via Hierarchical Bayes.
Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes.
Joint MRI Bias Removal Using Entropy Minimization Across Images.
Sparse Coding of Natural Images Using an Overcomplete Set of Limited Capacity Units.
Modelling Uncertainty in the Game of Go.
Markov Networks for Detecting Overalpping Elements in Sequence Data.
Exponential Family Harmoniums with an Application to Information Retrieval.
Intrinsically Motivated Reinforcement Learning.
Optimal Aggregation of Classifiers and Boosting Maps in Functional Magnetic Resonance Imaging.
Beat Tracking the Graphical Model Way.
Probabilistic Computation in Spiking Populations.
Joint Probabilistic Curve Clustering and Alignment.
Chemosensory Processing in a Spiking Model of the Olfactory Bulb: Chemotopic Convergence and Center Surround Inhibition.
Expectation Consistent Free Energies for Approximate Inference.
Contextual Models for Object Detection Using Boosted Random Fields.
Sub-Microwatt Analog VLSI Support Vector Machine for Pattern Classification and Sequence Estimation.
Non-Local Manifold Tangent Learning.
A Large Deviation Bound for the Area Under the ROC Curve.
Approximately Efficient Online Mechanism Design.
A Cost-Shaping LP for Bellman Error Minimization with Performance Guarantees.
VDCBPI: an Approximate Scalable Algorithm for Large POMDPs.
Rate- and Phase-coded Autoassociative Memory.
Kernel Projection Machine: a New Tool for Pattern Recognition.
Dynamic Bayesian Networks for Brain-Computer Interfaces.
Solitaire: Man Versus Machine.
Bayesian Regularization and Nonnegative Deconvolution for Time Delay Estimation.
Optimal sub-graphical models.
Brain Inspired Reinforcement Learning.
Modeling Nonlinear Dependencies in Natural Images using Mixture of Laplacian Distribution.
Saliency-Driven Image Acuity Modulation on a Reconfigurable Array of Spiking Silicon Neurons.
A Direct Formulation for Sparse PCA Using Semidefinite Programming.
Integrating Topics and Syntax.
Learning, Regularization and Ill-Posed Inverse Problems.
Spike-timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model.
Schema Learning: Experience-Based Construction of Predictive Action Models.
Euclidean Embedding of Co-Occurrence Data.
Blind One-microphone Speech Separation: A Spectral Learning Approach.
Coarticulation in Markov Decision Processes.
Exponentiated Gradient Algorithms for Large-margin Structured Classification.
Inference, Attention, and Decision in a Bayesian Neural Architecture.
Hierarchical Bayesian Inference in Networks of Spiking Neurons.
Similarity and Discrimination in Classical Conditioning: A Latent Variable Account.
Adaptive Manifold Learning.
Synergistic Face Detection and Pose Estimation with Energy-Based Models.
Assignment of Multiplicative Mixtures in Natural Images.
Whos In the Picture.
A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning.
Modeling Conversational Dynamics as a Mixed-Memory Markov Process.
Responding to Modalities with Different Latencies.
Synergies between Intrinsic and Synaptic Plasticity in Individual Model Neurons.
Maximum-Margin Matrix Factorization.
Semigroup Kernels on Finite Sets.
The Entire Regularization Path for the Support Vector Machine.
Validity Estimates for Loopy Belief Propagation on Binary Real-world Networks.
The Correlated Correspondence Algorithm for Unsupervised Registration of Nonrigid Surfaces.
Adaptive Discriminative Generative Model and Its Applications.
Self-Tuning Spectral Clustering.
Incremental Learning for Visual Tracking.
Hierarchical Eigensolver for Transition Matrices in Spectral Methods.
Making Latin Manuscripts Searchable using gHMMs.
Matrix Exponential Gradient Updates for On-line Learning and Bregman Projection.
Common-Frame Model for Object Recognition.
Semi-supervised Learning on Directed Graphs.
Pictorial Structures for Molecular Modeling: Interpreting Density Maps.
Maximising Sensitivity in a Spiking Network.
On the Adaptive Properties of Decision Trees.
Economic Properties of Social Networks.
Conditional Models of Identity Uncertainty with Application to Noun Coreference.
On-Chip Compensation of Device-Mismatch Effects in Analog VLSI Neural Networks.
Probabilistic Inference of Alternative Splicing Events in Microarray Data.
Semi-supervised Learning by Entropy Minimization.
Optimal Information Decoding from Neuronal Populations with Specific Stimulus Selectivity.
Analysis of a greedy active learning strategy.
Parametric Embedding for Class Visualization.
Mass Meta-analysis in Talairach Space.
A Harmonic Excitation State-Space Approach to Blind Separation of Speech.
Hierarchical Distributed Representations for Statistical Language Modeling.
An Auditory Paradigm for Brain-Computer Interfaces.
Instance-Based Relevance Feedback for Image Retrieval.
A Three Tiered Approach for Articulated Object Action Modeling and Recognition.
Instance-Specific Bayesian Model Averaging for Classification.
Using Random Forests in the Structured Language Model.
Supervised Graph Inference.
Discrete profile alignment via constrained information bottleneck.
Newscast EM.
Boosting on Manifolds: Adaptive Regularization of Base Classifiers.
Variational Minimax Estimation of Discrete Distributions under KL Loss.
Kernel Methods for Implicit Surface Modeling.
Methods Towards Invasive Human Brain Computer Interfaces.
A Feature Selection Algorithm Based on the Global Minimization of a Generalization Error Bound.
Efficient Kernel Machines Using the Improved Fast Gauss Transform.
Active Learning for Anomaly and Rare-Category Detection.
Face Detection - Efficient and Rank Deficient.
The power of feature clustering: An application to object detection.
Semi-Markov Conditional Random Fields for Information Extraction.
Multiple Alignment of Continuous Time Series.
New Criteria and a New Algorithm for Learning in Multi-Agent Systems.
Stable adaptive control with online learning.
Bayesian inference in spiking neurons.
Co-Training and Expansion: Towards Bridging Theory and Practice.
Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices.
Breaking SVM Complexity with Cross-Training.
At the Edge of Chaos: Real-time Computations and Self-Organized Criticality in Recurrent Neural Networks.
A Hidden Markov Model for de Novo Peptide Sequencing.
Efficient Out-of-Sample Extension of Dominant-Set Clusters.
An Application of Boosting to Graph Classification.
Learning Preferences for Multiclass Problems.
Using the Equivalent Kernel to Understand Gaussian Process Regression.
The Laplacian PDF Distance: A Cost Function for Clustering in a Kernel Feature Space.
Implicit Wiener Series for Higher-Order Image Analysis.
Sampling Methods for Unsupervised Learning.
Following Curved Regularized Optimization Solution Paths.
The Cerebellum Chip: an Analog VLSI Implementation of a Cerebellar Model of Classical Conditioning.
A Generalized Bradley-Terry Model: From Group Competition to Individual Skill.
The Variational Ising Classifier (VIC) Algorithm for Coherently Contaminated Data.
Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning.
Log-concavity Results on Gaussian Process Methods for Supervised and Unsupervised Learning.
Real-Time Pitch Determination of One or More Voices by Nonnegative Matrix Factorization.
Unsupervised Variational Bayesian Learning of Nonlinear Models.
Methods for Estimating the Computational Power and Generalization Capability of Neural Microcircuits.
Object Classification from a Single Example Utilizing Class Relevance Metrics.
Worst-Case Analysis of Selective Sampling for Linear-Threshold Algorithms.
Mistake Bounds for Maximum Entropy Discrimination.
Outlier Detection with One-class Kernel Fisher Discriminants.
Trait Selection for Assessing Beef Meat Quality Using Non-linear SVM.
Surface Reconstruction using Learned Shape Models.
Heuristics for Ordering Cue Search in Decision Making.
Edge of Chaos Computation in Mixed-Mode VLSI - A Hard Liquid.
Limits of Spectral Clustering.
The Power of Selective Memory: Self-Bounded Learning of Prediction Suffix Trees.
Linear Multilayer Independent Component Analysis for Large Natural Scenes.
Harmonising Chorales by Probabilistic Inference.
A Second Order Cone programming Formulation for Classifying Missing Data.
The Convergence of Contrastive Divergences.
The Rescorla-Wagner Algorithm and Maximum Likelihood Estimation of Causal Parameters.
Semi-supervised Learning via Gaussian Processes.
A Topographic Support Vector Machine: Classification Using Local Label Configurations.
Neural Network Computation by In Vitro Transcriptional Circuits.
Spike Sorting: Bayesian Clustering of Non-Stationary Data.
Maximal Margin Labeling for Multi-Topic Text Categorization.
Reducing Spike Train Variability: A Computational Theory Of Spike-Timing Dependent Plasticity.
A Probabilistic Model for Online Document Clustering with Application to Novelty Detection.
Learning Hyper-Features for Visual Identification.
Synchronization of neural networks by mutual learning and its application to cryptography.
Dependent Gaussian Processes.
Efficient Kernel Discriminant Analysis via QR Decomposition.
Two-Dimensional Linear Discriminant Analysis.
On Semi-Supervised Classification.
Confidence Intervals for the Area Under the ROC Curve.
Discriminant Saliency for Visual Recognition from Cluttered Scenes.
Support Vector Classification with Input Data Uncertainty.
Result Analysis of the NIPS 2003 Feature Selection Challenge.
Parallel Support Vector Machines: The Cascade SVM.
An Investigation of Practical Approximate Nearest Neighbor Algorithms.
Nonlinear Blind Source Separation by Integrating Independent Component Analysis and Slow Feature Analysis.
Fast Rates to Bayes for Kernel Machines.
Density Level Detection is Classification.
Semi-parametric Exponential Family PCA.
Generative Affine Localisation and Tracking.
A Temporal Kernel-Based Model for Tracking Hand Movements from Neural Activities.
PAC-Bayes Learning of Conjunctions and Classification of Gene-Expression Data.
Resolving Perceptual Aliasing In The Presence Of Noisy Sensors.
Incremental Algorithms for Hierarchical Classification.
Neighbourhood Components Analysis.
Algebraic Set Kernels with Application to Inference Over Local Image Representations.
Detecting Significant Multidimensional Spatial Clusters.
Seeing through water.
Message Errors in Belief Propagation.
Maximum Likelihood Estimation of Intrinsic Dimension.
Class-size Independent Generalization Analsysis of Some Discriminative Multi-Category Classification.
Exploration-Exploitation Tradeoffs for Experts Algorithms in Reactive Environments.
Hierarchical Clustering of a Mixture Model.
Large-Scale Prediction of Disulphide Bond Connectivity.
Convergence and No-Regret in Multiagent Learning.
