NeurIPS(NIPS) 2003 论文列表Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, NIPS 2002, December 9-14, 2002, Vancouver, British Columbia, Canada]. |
Exponential Family PCA for Belief Compression in POMDPs.
Learning in Zero-Sum Team Markov Games Using Factored Value Functions.
Learning to Take Concurrent Actions.
Nonparametric Representation of Policies and Value Functions: A Trajectory-Based Approach.
Efficient Learning Equilibrium.
A Convergent Form of Approximate Policy Iteration.
Approximate Linear Programming for Average-Cost Dynamic Programming.
Convergent Combinations of Reinforcement Learning with Linear Function Approximation.
Reinforcement Learning to Play an Optimal Nash Equilibrium in Team Markov Games.
Speeding up the Parti-Game Algorithm.
Optimality of Reinforcement Learning Algorithms with Linear Function Approximation.
Value-Directed Compression of POMDPs.
Bias-Optimal Incremental Problem Solving.
Minimax Differential Dynamic Programming: An Application to Robust Biped Walking.
Learning a Forward Model of a Reflex.
Learning Attractor Landscapes for Learning Motor Primitives.
A Probabilistic Model for Learning Concatenative Morphology.
"Name That Song!" A Probabilistic Approach to Querying on Music and Text.
Learning to Classify Galaxy Shapes Using the EM Algorithm.
A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences.
Improving a Page Classifier with Anchor Extraction and Link Analysis.
Inferring a Semantic Representation of Text via Cross-Language Correlation Analysis.
Adaptive Caching by Refetching.
Approximate Inference and Protein-Folding.
Prediction of Protein Topologies Using Generalized IOHMMS and RNNs.
A Maximum Entropy Approach to Collaborative Filtering in Dynamic, Sparse, High-Dimensional Domains.
Real-Time Monitoring of Complex Industrial Processes with Particle Filters.
Graph-Driven Feature Extraction From Microarray Data Using Diffusion Kernels and Kernel CCA.
Mismatch String Kernels for SVM Protein Classification.
The RA Scanner: Prediction of Rheumatoid Joint Inflammation Based on Laser Imaging.
Identity Uncertainty and Citation Matching.
Learning About Multiple Objects in Images: Factorial Learning without Factorial Search.
Concurrent Object Recognition and Segmentation by Graph Partitioning.
How to Combine Color and Shape Information for 3D Object Recognition: Kernels do the Trick.
A Model for Learning Variance Components of Natural Images.
Learning Sparse Topographic Representations with Products of Student-t Distributions.
Feature Selection by Maximum Marginal Diversity.
Recovering Intrinsic Images from a Single Image.
Shape Recipes: Scene Representations that Refer to the Image.
Learning Sparse Multiscale Image Representations.
Linear Combinations of Optic Flow Vectors for Estimating Self-Motion - a Real-World Test of a Neural Model.
Recovering Articulated Model Topology from Observed Rigid Motion.
Unsupervised Color Constancy.
Dynamic Structure Super-Resolution.
A Bilinear Model for Sparse Coding.
Bayesian Image Super-Resolution.
A Prototype for Automatic Recognition of Spontaneous Facial Actions.
Fast Transformation-Invariant Factor Analysis.
Learning to Detect Natural Image Boundaries Using Brightness and Texture.
Learning to Perceive Transparency from the Statistics of Natural Scenes.
Application of Variational Bayesian Approach to Speech Recognition.
Discriminative Binaural Sound Localization.
Monaural Speech Separation.
An Asynchronous Hidden Markov Model for Audio-Visual Speech Recognition.
Source Separation with a Sensor Array Using Graphical Models and Subband Filtering.
Bayesian Estimation of Time-Frequency Coefficients for Audio Signal Enhancement.
Analysis of Information in Speech Based on MANOVA.
Real Time Voice Processing with Audiovisual Feedback: Toward Autonomous Agents with Perfect Pitch.
A Probabilistic Approach to Single Channel Blind Signal Separation.
Forward-Decoding Kernel-Based Phone Recognition.
Field-Programmable Learning Arrays.
Spike Timing-Dependent Plasticity in the Address Domain.
R. Jacob Vogelstein Francesco Tenore Ralf Philipp Miriam S. Adlerstein David H. Goldberg Gert Cauwenberghs
Topographic Map Formation by Silicon Growth Cones.
Developing Topography and Ocular Dominance Using Two aVLSI Vision Sensors and a Neurotrophic Model of Plasticity.
Classifying Patterns of Visual Motion - a Neuromorphic Approach.
Combining Features for BCI.
Improving Transfer Rates in Brain Computer Interfacing: A Case Study.
Retinal Processing Emulation in a Programmable 2-Layer Analog Array Processor CMOS Chip.
Ricardo Carmona-Galán Francisco Jiménez-Garrido Rafael Domínguez-Castro Servando Espejo-Meana Ángel Rodríguez-Vázquez
Neuromorphic Bistable VLSI Synapses with Spike-Timing-Dependent Plasticity.
Adaptive Quantization and Density Estimation in Silicon.
Circuit Model of Short-Term Synaptic Dynamics.
Optoelectronic Implementation of a FitzHugh-Nagumo Neural Model.
Real-Time Particle Filters.
Location Estimation with a Differential Update Network.
Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector Machines.
Derivative Observations in Gaussian Process Models of Dynamic Systems.
Information Regularization with Partially Labeled Data.
Multiple Cause Vector Quantization.
Learning Graphical Models with Mercer Kernels.
Incremental Gaussian Processes.
FloatBoost Learning for Classification.
Discriminative Densities from Maximum Contrast Estimation.
Discriminative Learning for Label Sequences via Boosting.
Annealing and the Rate Distortion Problem.
Charting a Manifold.
Transductive and Inductive Methods for Approximate Gaussian Process Regression.
Multiclass Learning by Probabilistic Embeddings.
Ranking with Large Margin Principle: Two Approaches.
Using Manifold Stucture for Partially Labeled Classification.
The Decision List Machine.
Artefactual Structure from Least-Squares Multidimensional Scaling.
Robust Novelty Detection with Single-Class MPM.
Learning with Multiple Labels.
Feature Selection and Classification on Matrix Data: From Large Margins to Small Covering Numbers.
Handling Missing Data with Variational Bayesian Learning of ICA.
Kernel Dependency Estimation.
Critical Lines in Symmetry of Mixture Models and its Application to Component Splitting.
Extracting Relevant Structures with Side Information.
Informed Projections.
Automatic Alignment of Local Representations.
Stochastic Neighbor Embedding.
Manifold Parzen Windows.
Going Metric: Denoising Pairwise Data.
Exact MAP Estimates by (Hyper)tree Agreement.
Using Tarjan's Red Rule for Fast Dependency Tree Construction.
Nash Propagation for Loopy Graphical Games.
Constraint Classification for Multiclass Classification and Ranking.
A Differential Semantics for Jointree Algorithms.
VIBES: A Variational Inference Engine for Bayesian Networks.
A Formulation for Minimax Probability Machine Regression.
One-Class LP Classifiers for Dissimilarity Representations.
Regularized Greedy Importance Sampling.
Boosted Dyadic Kernel Discriminants.
Adaptive Classification by Variational Kalman Filtering.
Clustering with the Fisher Score.
Parametric Mixture Models for Multi-Labeled Text.
Dynamic Bayesian Networks with Deterministic Latent Tables.
Global Versus Local Methods in Nonlinear Dimensionality Reduction.
On the Dirichlet Prior and Bayesian Regularization.
Half-Lives of EigenFlows for Spectral Clustering.
Intrinsic Dimension Estimation Using Packing Numbers.
Automatic Derivation of Statistical Algorithms: The EM Family and Beyond.
Self Supervised Boosting.
Learning Semantic Similarity.
Independent Components Analysis through Product Density Estimation.
Boosting Density Estimation.
String Kernels, Fisher Kernels and Finite State Automata.
Feature Selection in Mixture-Based Clustering.
Stability-Based Model Selection.
Fast Sparse Gaussian Process Methods: The Informative Vector Machine.
Rational Kernels.
Adaptive Nonlinear System Identification with Echo State Networks.
Cluster Kernels for Semi-Supervised Learning.
Generalized2 Linear2 Models.
Fast Kernels for String and Tree Matching.
Support Vector Machines for Multiple-Instance Learning.
Adaptive Scaling for Feature Selection in SVMs.
Coulomb Classifiers: Generalizing Support Vector Machines via an Analogy to Electrostatic Systems.
Kernel Design Using Boosting.
Gaussian Process Priors with Uncertain Inputs - Application to Multiple-Step Ahead Time Series Forecasting.
Knowledge-Based Support Vector Machine Classifiers.
Adapting Codes and Embeddings for Polychotomies.
Distance Metric Learning with Application to Clustering with Side-Information.
Mean Field Approach to a Probabilistic Model in Information Retrieval.
Bayesian Monte Carlo.
Hyperkernels.
Margin-Based Algorithms for Information Filtering.
Margin Analysis of the LVQ Algorithm.
Effective Dimension and Generalization of Kernel Learning.
An Impossibility Theorem for Clustering.
Fractional Belief Propagation.
A Note on the Representational Incompatibility of Function Approximation and Factored Dynamics.
PAC-Bayes & Margins.
Conditional Models on the Ranking Poset.
Rate Distortion Function in the Spin Glass State: A Toy Model.
On the Complexity of Learning the Kernel Matrix.
The Effect of Singularities in a Learning Machine when the True Parameters Do Not Lie on such Singularities.
Scaling of Probability-Based Optimization Algorithms.
Information Diffusion Kernels.
The Stability of Kernel Principal Components Analysis and its Relation to the Process Eigenspectrum.
Dyadic Classification Trees via Structural Risk Minimization.
Concentration Inequalities for the Missing Mass and for Histogram Rule Error.
Stable Fixed Points of Loopy Belief Propagation Are Local Minima of the Bethe Free Energy.
Maximum Likelihood and the Information Bottleneck.
A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages.
Data-Dependent Bounds for Bayesian Mixture Methods.
Reconstructing Stimulus-Driven Neural Networks from Spike Times.
Evidence Optimization Techniques for Estimating Stimulus-Response Functions.
An Estimation-Theoretic Framework for the Presentation of Multiple Stimuli.
A Neural Edge-Detection Model for Enhanced Auditory Sensitivity in Modulated Noise.
Interpreting Neural Response Variability as Monte Carlo Sampling of the Posterior.
Dynamical Constraints on Computing with Spike Timing in the Cortex.
Maximally Informative Dimensions: Analyzing Neural Responses to Natural Signals.
Kernel-Based Extraction of Slow Features: Complex Cells Learn Disparity and Translation Invariance from Natural Images.
Selectivity and Metaplasticity in a Unified Calcium-Dependent Model.
Hidden Markov Model of Cortical Synaptic Plasticity: Derivation of the Learning Rule.
A Digital Antennal Lobe for Pattern Equalization: Analysis and Design.
Adaptation and Unsupervised Learning.
A Model for Real-Time Computation in Generic Neural Microcircuits.
Factorial Coding of Color in Primary Visual Cortex.
An Information Theoretic Approach to the Functional Classification of Neurons.
Binary Tuning is Optimal for Neural Rate Coding with High Temporal Resolution.
Branching Law for Axons.
Convergence Properties of Some Spike-Triggered Analysis Techniques.
Dopamine Induced Bistability Enhances Signal Processing in Spiny Neurons.
Expected and Unexpected Uncertainty: ACh and NE in the Neocortex.
Learning in Spiking Neural Assemblies.
Temporal Coherence, Natural Image Sequences, and the Visual Cortex.
Spectro-Temporal Receptive Fields of Subthreshold Responses in Auditory Cortex.
Spikernels: Embedding Spiking Neurons in Inner-Product Spaces.
Neural Decoding of Cursor Motion Using a Kalman Filter.
How Linear are Auditory Cortical Responses?.
Binary Coding in Auditory Cortex.
Automatic Acquisition and Efficient Representation of Syntactic Structures.
Timing and Partial Observability in the Dopamine System.
Visual Development Aids the Acquisition of Motion Velocity Sensitivities.
Dynamical Causal Learning.
Modeling Midazolam's Effect on the Hippocampus and Recognition Memory.
Combining Dimensions and Features in Similarity-Based Representations.
Bayesian Models of Inductive Generalization.
How the Poverty of the Stimulus Solves the Poverty of the Stimulus.
Theory-Based Causal Inference.
Categorization Under Complexity: A Unified MDL Account of Human Learning of Regular and Irregular Categories.
A Minimal Intervention Principle for Coordinated Movement.
Replay, Repair and Consolidation.
Prediction and Semantic Association.
Fast Exact Inference with a Factored Model for Natural Language Parsing.