NeurIPS(NIPS) 2004 论文列表
Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, NIPS 2003, December 8-13, 2003, Vancouver and Whistler, British Columbia, Canada].
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An Improved Scheme for Detection and Labelling in Johansson Displays.
Bayesian Color Constancy with Non-Gaussian Models.
A Sampled Texture Prior for Image Super-Resolution.
Geometric Analysis of Constrained Curves.
Salient Boundary Detection using Ratio Contour.
Towards Social Robots: Automatic Evaluation of Human-robot Interaction by Face Detection and Expression Classification.
Learning Non-Rigid 3D Shape from 2D Motion.
Automatic Annotation of Everyday Movements.
Attractive People: Assembling Loose-Limbed Models using Non-parametric Belief Propagation.
Discriminative Fields for Modeling Spatial Dependencies in Natural Images.
Learning a Rare Event Detection Cascade by Direct Feature Selection.
Mutual Boosting for Contextual Inference.
Factorization with Uncertainty and Missing Data: Exploiting Temporal Coherence.
Using the Forest to See the Trees: A Graphical Model Relating Features, Objects, and Scenes.
Discriminating Deformable Shape Classes.
Bounded Invariance and the Formation of Place Fields..
Eye Micro-movements Improve Stimulus Detection Beyond the Nyquist Limit in the Peripheral Retina.
Eye Movements for Reward Maximization.
Human and Ideal Observers for Detecting Image Curves.
A Functional Architecture for Motion Pattern Processing in MSTd.
Nonlinear Processing in LGN Neurons.
Local Phase Coherence and the Perception of Blur.
A Classification-based Cocktail-party Processor.
One Microphone Blind Dereverberation Based on Quasi-periodicity of Speech Signals.
Predicting Speech Intelligibility from a Population of Neurons.
Eigenvoice Speaker Adaptation via Composite Kernel PCA.
Probabilistic Inference of Speech Signals from Phaseless Spectrograms.
A Kullback-Leibler Divergence Based Kernel for SVM Classification in Multimedia Applications.
Phonetic Speaker Recognition with Support Vector Machines.
Prediction on Spike Data Using Kernel Algorithms.
Decoding V1 Neuronal Activity using Particle Filtering with Volterra Kernels.
A Probabilistic Model of Auditory Space Representation in the Barn Owl.
Analytical Solution of Spike-timing Dependent Plasticity Based on Synaptic Biophysics.
Estimating Internal Variables and Paramters of a Learning Agent by a Particle Filter.
Probabilistic Inference in Human Sensorimotor Processing.
Design of Experiments via Information Theory.
Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model.
Plasticity Kernels and Temporal Statistics.
Mechanism of Neural Interference by Transcranial Magnetic Stimulation: Network or Single Neuron?
A Biologically Plausible Algorithm for Reinforcement-shaped Representational Learning.
Circuit Optimization Predicts Dynamic Network for Chemosensory Orientation in the Nematode C. elegans.
Dopamine Modulation in a Basal Ganglio-cortical Network Implements Saliency-based Gating of Working Memory.
The Diffusion-Limited Biochemical Signal-Relay Channel.
Information Dynamics and Emergent Computation in Recurrent Circuits of Spiking Neurons.
The Doubly Balanced Network of Spiking Neurons: A Memory Model with High Capacity.
Margin Maximizing Loss Functions.
Online Passive-Aggressive Algorithms.
Measure Based Regularization.
Information Bottleneck for Gaussian Variables.
Ambiguous Model Learning Made Unambiguous with 1/f Priors.
Learning Curves for Stochastic Gradient Descent in Linear Feedforward Networks.
Approximate Analytical Bootstrap Averages for Support Vector Classifiers.
Limiting Form of the Sample Covariance Eigenspectrum in PCA and Kernel PCA.
Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates.
Geometric Clustering Using the Information Bottleneck Method.
Variational Linear Response.
Learning Bounds for a Generalized Family of Bayesian Posterior Distributions.
When Does Non-Negative Matrix Factorization Give a Correct Decomposition into Parts?
Self-calibrating Probability Forecasting.
PAC-Bayesian Generic Chaining.
Near-Minimax Optimal Classification with Dyadic Classification Trees.
Boosting versus Covering.
On the Dynamics of Boosting.
Online Learning of Non-stationary Sequences.
Error Bounds for Transductive Learning via Compression and Clustering.
An Infinity-sample Theory for Multi-category Large Margin Classification.
Sparseness of Support Vector Machines---Some Asymptotically Sharp Bounds.
A Neuromorphic Multi-chip Model of a Disparity Selective Complex Cell.
Entrainment of Silicon Central Pattern Generators for Legged Locomotory Control.
A Mixed-Signal VLSI for Real-Time Generation of Edge-Based Image Vectors.
Synchrony Detection by Analogue VLSI Neurons with Bimodal STDP Synapses.
Training a Quantum Neural Network.
Minimising Contrastive Divergence in Noisy, Mixed-mode VLSI Neurons.
A Summating, Exponentially-Decaying CMOS Synapse for Spiking Neural Systems.
A Recurrent Model of Orientation Maps with Simple and Complex Cells.
A Low-Power Analog VLSI Visual Collision Detector.
Model Uncertainty in Classical Conditioning.
A Holistic Approach to Compositional Semantics: A Connectionist Model and Robot Experiments.
Unsupervised Context Sensitive Language Acquisition from a Large Corpus.
From Algorithmic to Subjective Randomness.
Perception of the Structure of the Physical World Using Unknown Multimodal Sensors and Effectors.
An MCMC-Based Method of Comparing Connectionist Models in Cognitive Science.
Learning a World Model and Planning with a Self-Organizing, Dynamic Neural System.
Reasoning about Time and Knowledge in Neural Symbolic Learning Systems.
Sensory Modality Segregation.
Insights from Machine Learning Applied to Human Visual Classification.
Linear Program Approximations for Factored Continuous-State Markov Decision Processes.
Distributed Optimization in Adaptive Networks.
Auction Mechanism Design for Multi-Robot Coordination.
Extending Q-Learning to General Adaptive Multi-Agent Systems.
Learning Near-Pareto-Optimal Conventions in Polynomial Time.
A Nonlinear Predictive State Representation.
Approximate Policy Iteration with a Policy Language Bias.
Robustness in Markov Decision Problems with Uncertain Transition Matrices.
Policy Search by Dynamic Programming.
Bounded Finite State Controllers.
How to Combine Expert (and Novice) Advice when Actions Impact the Environment?
All learning is Local: Multi-agent Learning in Global Reward Games.
Autonomous Helicopter Flight via Reinforcement Learning.
An MDP-Based Approach to Online Mechanism Design.
Envelope-based Planning in Relational MDPs.
Approximate Planning in POMDPs with Macro-Actions.
ARA*: Anytime A* with Provable Bounds on Sub-Optimality.
Applying Metric-Trees to Belief-Point POMDPs.
Gaussian Processes in Reinforcement Learning.
Subject-Independent Magnetoencephalographic Source Localization by a Multilayer Perceptron.
Increase Information Transfer Rates in BCI by CSP Extension to Multi-class.
Impact of an Energy Normalization Transform on the Performance of the LF-ASD Brain Computer Interface.
Nonlinear Filtering of Electron Micrographs by Means of Support Vector Regression.
Training fMRI Classifiers to Discriminate Cognitive States across Multiple Subjects.
Different Cortico-Basal Ganglia Loops Specialize in Reward Prediction at Different Time Scales.
Reconstructing MEG Sources with Unknown Correlations.
Gene Expression Clustering with Functional Mixture Models.
ICA-based Clustering of Genes from Microarray Expression Data.
Unsupervised Color Decomposition Of Histologically Stained Tissue Samples.
Link Prediction in Relational Data.
A Fast Multi-Resolution Method for Detection of Significant Spatial Disease Clusters.
Kernels for Structured Natural Language Data.
Application of SVMs for Colour Classification and Collision Detection with AIBO Robots.
Modeling User Rating Profiles For Collaborative Filtering.
Parameterized Novelty Detectors for Environmental Sensor Monitoring.
Markov Models for Automated ECG Interval Analysis.
Statistical Debugging of Sampled Programs.
Semi-supervised Protein Classification Using Cluster Kernels.
An Autonomous Robotic System for Mapping Abandoned Mines.
GPPS: A Gaussian Process Positioning System for Cellular Networks.
Fast Embedding of Sparse Similarity Graphs.
Algorithms for Interdependent Security Games.
A Model for Learning the Semantics of Pictures.
Classification with Hybrid Generative/Discriminative Models.
Necessary Intransitive Likelihood-Ratio Classifiers.
Probability Estimates for Multi-Class Classification by Pairwise Coupling.
Bias-Corrected Bootstrap and Model Uncertainty.
No Unbiased Estimator of the Variance of K-Fold Cross-Validation.
Minimax Embeddings.
Log-Linear Models for Label Ranking.
Identifying Structure across Pre-partitioned Data.
An Iterative Improvement Procedure for Hierarchical Clustering.
Feature Selection in Clustering Problems.
Computing Gaussian Mixture Models with EM Using Equivalence Constraints.
Semi-Definite Programming by Perceptron Learning.
Learning to Find Pre-Images.
Laplace Propagation.
Generalised Propagation for Fast Fourier Transforms with Partial or Missing Data.
Sample Propagation.
Wormholes Improve Contrastive Divergence.
Fast Algorithms for Large-State-Space HMMs with Applications to Web Usage Analysis.
Inferring State Sequences for Non-linear Systems with Embedded Hidden Markov Models.
On the Concentration of Expectation and Approximate Inference in Layered Networks.
Denoising and Untangling Graphs Using Degree Priors.
Approximability of Probability Distributions.
Semidefinite Relaxations for Approximate Inference on Graphs with Cycles.
Linear Response for Approximate Inference.
Approximate Expectation Maximization.
Can We Learn to Beat the Best Stock.
Warped Gaussian Processes.
Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data.
Learning with Local and Global Consistency.
AUC Optimization vs. Error Rate Minimization.
Learning Spectral Clustering.
Non-linear CCA and PCA by Alignment of Local Models.
Finding the M Most Probable Configurations in Arbitrary Graphical Models.
Learning the k in k-means.
Nonstationary Covariance Functions for Gaussian Process Regression.
New Algorithms for Efficient High Dimensional Non-parametric Classification.
Semi-Supervised Learning with Trees.
Perspectives on Sparse Bayesian Learning.
Sparse Representation and Its Applications in Blind Source Separation.
Online Learning via Global Feedback for Phrase Recognition.
Online Classification on a Budget.
Large Scale Online Learning.
Iterative Scaled Trust-Region Learning in Krylov Subspaces via Pearlmutter's Implicit Sparse Hessian-Vector Multiply.
Information Maximization in Noisy Channels : A Variational Approach.
Tree-structured Approximations by Expectation Propagation.
Pairwise Clustering and Graphical Models.
Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering.
Ranking on Data Manifolds.
Optimal Manifold Representation of Data: An Information Theoretic Approach.
Locality Preserving Projections.
Linear Dependent Dimensionality Reduction.
Extreme Components Analysis.
Dynamical Modeling with Kernels for Nonlinear Time Series Prediction.
Fast Feature Selection from Microarray Expression Data via Multiplicative Large Margin Algorithms.
Sequential Bayesian Kernel Regression.
Sparse Greedy Minimax Probability Machine Classification.
Efficient and Robust Feature Extraction by Maximum Margin Criterion.
Clustering with the Connectivity Kernel.
Kernel Dimensionality Reduction for Supervised Learning.
Convex Methods for Transduction.
Multiple-Instance Learning via Disjunctive Programming Boosting.
Image Reconstruction by Linear Programming.
1-norm Support Vector Machines.
Learning a Distance Metric from Relative Comparisons.
Invariant Pattern Recognition by Semi-Definite Programming Machines.
Max-Margin Markov Networks.
Hierarchical Topic Models and the Nested Chinese Restaurant Process.
Simplicial Mixtures of Markov Chains: Distributed Modelling of Dynamic User Profiles.
Efficient Multiscale Sampling from Products of Gaussian Mixtures.
