NeurIPS(NIPS) 2001 论文列表Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, December 3-8, 2001, Vancouver, British Columbia, Canada]. |
Stabilizing Value Function Approximation with the BFBP Algorithm.
Direct value-approximation for factored MDPs.
Efficient Resources Allocation for Markov Decision Processes.
The Steering Approach for Multi-Criteria Reinforcement Learning.
Predictive Representations of State.
Model-Free Least-Squares Policy Iteration.
Incremental A*.
A Natural Policy Gradient.
Multiagent Planning with Factored MDPs.
Rates of Convergence of Performance Gradient Estimates Using Function Approximation and Bias in Reinforcement Learning.
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning.
Convergence of Optimistic and Incremental Q-Learning.
Batch Value Function Approximation via Support Vectors.
Playing is believing: The role of beliefs in multi-agent learning.
Reinforcement Learning with Long Short-Term Memory.
Active Portfolio-Management based on Error Correction Neural Networks.
Face Recognition Using Kernel Methods.
Active Learning in the Drug Discovery Process.
The Intelligent surfer: Probabilistic Combination of Link and Content Information in PageRank.
A Bayesian Network for Real-Time Musical Accompaniment.
Learning a Gaussian Process Prior for Automatically Generating Music Playlists.
Hyperbolic Self-Organizing Maps for Semantic Navigation.
Prodding the ROC Curve: Constrained Optimization of Classifier Performance.
Optimising Synchronisation Times for Mobile Devices.
Cobot: A Social Reinforcement Learning Agent.
Using Vocabulary Knowledge in Bayesian Multinomial Estimation.
Improvisation and Learning.
Estimating Car Insurance Premia: a Case Study in High-Dimensional Data Inference.
Nicolas Chapados Yoshua Bengio Pascal Vincent Joumana Ghosn Charles Dugas Ichiro Takeuchi Linyan Meng
Tempo tracking and rhythm quantization by sequential Monte Carlo.
Bayesian Predictive Profiles With Applications to Retail Transaction Data.
Model Based Population Tracking and Automatic Detection of Distribution Changes.
Switch Packet Arbitration via Queue-Learning.
Grouping with Bias.
A Rotation and Translation Invariant Discrete Saliency Network.
Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade.
Contextual Modulation of Target Saliency.
Transform-invariant Image Decomposition with Similarity Templates.
Unsupervised Learning of Human Motion Models.
The Fidelity of Local Ordinal Encoding.
A Hierarchical Model of Complex Cells in Visual Cortex for the Binocular Perception of Motion-in-Depth.
Learning Body Pose via Specialized Maps.
Grouping and dimensionality reduction by locally linear embedding.
Modeling the Modulatory Effect of Attention on Human Spatial Vision.
Categorization by Learning and Combining Object Parts.
The g Factor: Relating Distributions on Features to Distributions on Images.
Perceptual Metamers in Stereoscopic Vision.
A Neural Oscillator Model of Auditory Selective Attention.
Sequential Noise Compensation by Sequential Monte Carlo Method.
Speech Recognition using SVMs.
Speech Recognition with Missing Data using Recurrent Neural Nets.
Estimating the Reliability of ICA Projections.
Audio-Visual Sound Separation Via Hidden Markov Models.
ALGONQUIN - Learning Dynamic Noise Models From Noisy Speech for Robust Speech Recognition.
A Sequence Kernel and its Application to Speaker Recognition.
Relative Density Nets: A New Way to Combine Backpropagation with HMM's.
Intransitive Likelihood-Ratio Classifiers.
Analog Soft-Pattern-Matching Classifier using Floating-Gate MOS Technology.
Learning Spike-Based Correlations and Conditional Probabilities in Silicon.
An Efficient Clustering Algorithm Using Stochastic Association Model and Its Implementation Using Nanostructures.
Orientation-Selective aVLSI Spiking Neurons.
Stochastic Mixed-Signal VLSI Architecture for High-Dimensional Kernel Machines.
Citcuits for VLSI Implementation of Temporally Asymmetric Hebbian Learning.
Kernel Logistic Regression and the Import Vector Machine.
EM-DD: An Improved Multiple-Instance Learning Technique.
A General Greedy Approximation Algorithm with Applications.
Spectral Relaxation for K-means Clustering.
Blind Source Separation via Multinode Sparse Representation.
Reducing multiclass to binary by coupling probability estimates.
The Concave-Convex Procedure (CCCP).
Iterative Double Clustering for Unsupervised and Semi-Supervised Learning.
Products of Gaussians.
Learning Lateral Interactions for Feature Binding and Sensory Segmentation.
Tree-based reparameterization for approximate inference on loopy graphs.
Multi Dimensional ICA to Separate Correlated Sources.
K-Local Hyperplane and Convex Distance Nearest Neighbor Algorithms.
A New Discriminative Kernel From Probabilistic Models.
Learning Discriminative Feature Transforms to Low Dimensions in Low Dimentions.
Risk Sensitive Particle Filters.
The Unified Propagation and Scaling Algorithm.
Partially labeled classification with Markov random walks.
Bayesian time series classification.
Agglomerative Multivariate Information Bottleneck.
Dynamic Time-Alignment Kernel in Support Vector Machine.
Probabilistic Abstraction Hierarchies.
Covariance Kernels from Bayesian Generative Models.
Multiplicative Updates for Classification by Mixture Models.
Global Coordination of Local Linear Models.
Infinite Mixtures of Gaussian Process Experts.
MIME: Mutual Information Minimization and Entropy Maximization for Bayesian Belief Propagation.
Matching Free Trees with Replicator Equations.
Learning Hierarchical Structures with Linear Relational Embedding.
On Spectral Clustering: Analysis and an algorithm.
On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes.
Linear-time inference in Hierarchical HMMs.
Quantizing Density Estimators.
An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games.
(Not) Bounding the True Error.
Minimax Probability Machine.
A Dynamic HMM for On-line Segmentation of Sequential Data.
Online Learning with Kernels.
Active Information Retrieval.
The Method of Quantum Clustering.
Kernel Feature Spaces and Nonlinear Blind Souce Separation.
Escaping the Convex Hull with Extrapolated Vector Machines.
Discriminative Direction for Kernel Classifiers.
Very loopy belief propagation for unwrapping phase images.
Product Analysis: Learning to Model Observations as Products of Hidden Variables.
Fast, Large-Scale Transformation-Invariant Clustering.
KLD-Sampling: Adaptive Particle Filters.
Incremental Learning and Selective Sampling via Parametric Optimization Framework for SVM.
Adaptive Sparseness Using Jeffreys Prior.
Approximate Dynamic Programming via Linear Programming.
A kernel method for multi-labelled classification.
Learning from Infinite Data in Finite Time.
Adaptive Nearest Neighbor Classification Using Support Vector Machines.
TAP Gibbs Free Energy, Belief Propagation and Sparsity.
Spectral Kernel Methods for Clustering.
Pranking with Ranking.
A Parallel Mixture of SVMs for Very Large Scale Problems.
Convolution Kernels for Natural Language.
A Generalization of Principal Components Analysis to the Exponential Family.
Incorporating Invariances in Non-Linear Support Vector Machines.
Latent Dirichlet Allocation.
Duality, Geometry, and Support Vector Regression.
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering.
The Infinite Hidden Markov Model.
Thin Junction Trees.
Rao-Blackwellised Particle Filtering via Data Augmentation.
Semi-supervised MarginBoost.
Generalization Performance of Some Learning Problems in Hilbert Functional Spaces.
Fast Parameter Estimation Using Green's Functions.
Information-Geometrical Significance of Sparsity in Gallager Codes.
Gaussian Process Regression with Mismatched Models.
On the Concentration of Spectral Properties.
Computing Time Lower Bounds for Recurrent Sigmoidal Neural Networks.
Scaling Laws and Local Minima in Hebbian ICA.
On the Convergence of Leveraging.
Asymptotic Universality for Learning Curves of Support Vector Machines.
Entropy and Inference, Revisited.
A Variational Approach to Learning Curves.
Means, Correlations and Bounds.
Boosting and Maximum Likelihood for Exponential Models.
Kernel Machines and Boolean Functions.
Small-World Phenomena and the Dynamics of Information.
Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms.
Novel iteration schemes for the Cluster Variation Method.
Information Geometrical Framework for Analyzing Belief Propagation Decoder.
Distribution of Mutual Information.
Algorithmic Luckiness.
Analysis of Sparse Bayesian Learning.
PAC Generalization Bounds for Co-training.
On Kernel-Target Alignment.
On the Generalization Ability of On-Line Learning Algorithms.
The Noisy Euclidean Traveling Salesman Problem and Learning.
Geometrical Singularities in the Neuromanifold of Multilayer Perceptrons.
Sampling Techniques for Kernel Methods.
Generating velocity tuning by asymmetric recurrent connections.
Neural Implementation of Bayesian Inference in Population Codes.
Spike timing and the coding of naturalistic sounds in a central auditory area of songbirds.
Activity Driven Adaptive Stochastic Resonance.
Effective Size of Receptive Fields of Inferior Temporal Visual Cortex Neurons in Natural Scenes.
Why Neuronal Dynamics Should Control Synaptic Learning Rules.
Correlation Codes in Neuronal Populations.
Characterizing Neural Gain Control using Spike-triggered Covariance.
Eye movements and the maturation of cortical orientation selectivity.
Information-Geometric Decomposition in Spike Analysis.
Self-regulation Mechanism of Temporally Asymmetric Hebbian Plasticity.
Associative memory in realistic neuronal networks.
3 state neurons for contextual processing.
A theory of neural integration in the head-direction system.
Probabilistic Inference of Hand Motion from Neural Activity in Motor Cortex.
Exact differential equation population dynamics for integrate-and-fire neurons.
Linking Motor Learning to Function Approximation: Learning in an Unlearnable Force Field.
ACh, Uncertainty, and Cortical Inference.
A Maximum-Likelihood Approach to Modeling Multisensory Enhancement.
Group Redundancy Measures Reveal Redundancy Reduction in the Auditory Pathway.
Orientational and Geometric Determinants of Place and Head-direction.
Classifying Single Trial EEG: Towards Brain Computer Interfacing.
Receptive field structure of flow detectors for heading perception.
Modularity in the motor system: decomposition of muscle patterns as combinations of time-varying synergies.
Bayesian morphometry of hippocampal cells suggests same-cell somatodendritic repulsion.
A Quantitative Model of Counterfactual Reasoning.
Reinforcement Learning and Time Perception -- a Model of Animal Experiments.
Constructing Distributed Representations Using Additive Clustering.
Causal Categorization with Bayes Nets.
Grammatical Bigrams.
A Model of the Phonological Loop: Generalization and Binding.
Generalizable Relational Binding from Coarse-coded Distributed Representations.
Grammar Transfer in a Second Order Recurrent Neural Network.
A Bayesian Model Predicts Human Parse Preference and Reading Times in Sentence Processing.
A Rational Analysis of Cognitive Control in a Speeded Discrimination Task.
The Emergence of Multiple Movement Units in the Presence of Noise and Feedback Delay.
Natural Language Grammar Induction Using a Constituent-Context Model.
Fragment Completion in Humans and Machines.
Probabilistic principles in unsupervised learning of visual structure: human data and a model.
Motivated Reinforcement Learning.
Modeling Temporal Structure in Classical Conditioning.