NeurIPS(NIPS) 2000 论文列表
Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29 - December 4, 1999].
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Monte Carlo POMDPs.
Policy Gradient Methods for Reinforcement Learning with Function Approximation.
Learning Factored Representations for Partially Observable Markov Decision Processes.
Coastal Navigation with Mobile Robots.
Reinforcement Learning Using Approximate Belief States.
Neural Network Based Model Predictive Control.
Policy Search via Density Estimation.
Bayesian Map Learning in Dynamic Environments.
Actor-Critic Algorithms.
Approximate Planning in Large POMDPs via Reusable Trajectories.
State Abstraction in MAXQ Hierarchical Reinforcement Learning.
An Environment Model for Nonstationary Reinforcement Learning.
Learning from User Feedback in Image Retrieval Systems.
Generalized Model Selection for Unsupervised Learning in High Dimensions.
Image Recognition in Context: Application to Microscopic Urinalysis.
Reinforcement Learning for Spoken Dialogue Systems.
Application of Blind Separation of Sources to Optical Recording of Brain Activity.
Unmixing Hyperspectral Data.
Churn Reduction in the Wireless Industry.
From Coexpression to Coregulation: An Approach to Inferring Transcriptional Regulation among Gene Classes from Large-Scale Expression Data.
Constructing Heterogeneous Committees Using Input Feature Grouping: Application to Economic Forecasting.
Learning the Similarity of Documents: An Information-Geometric Approach to Document Retrieval and Categorization.
Kirchoff Law Markov Fields for Analog Circuit Design.
Learning Informative Statistics: A Nonparametnic Approach.
Low Power Wireless Communication via Reinforcement Learning.
Image Representations for Facial Expression Coding.
Robust Learning of Chaotic Attractors.
Managing Uncertainty in Cue Combination.
A SNoW-Based Face Detector.
Scale Mixtures of Gaussians and the Statistics of Natural Images.
Hierarchical Image Probability (H1P) Models.
Learning Sparse Codes with a Mixture-of-Gaussians Prior.
An Information-Theoretic Framework for Understanding Saccadic Eye Movements.
Emergence of Topography and Complex Cell Properties from Natural Images using Extensions of ICA.
Bayesian Reconstruction of 3D Human Motion from Single-Camera Video.
Audio Vision: Using Audio-Visual Synchrony to Locate Sounds.
Search for Information Bearing Components in Speech.
Speech Modelling Using Subspace and EM Techniques.
Online Independent Component Analysis with Local Learning Rate Adaptation.
Constrained Hidden Markov Models.
Broadband Direction-Of-Arrival Estimation Based on Second Order Statistics.
Spectral Cues in Human Sound Localization.
Neural System Model of Human Sound Localization.
Bayesian Modelling of fMRI lime Series.
An Oscillatory Correlation Frame work for Computational Auditory Scene Analysis.
An Analog VLSI Model of Periodicity Extraction.
Bifurcation Analysis of a Silicon Neuron.
A Neuromorphic VLSI System for Modeling the Neural Control of Axial Locomotion.
A Winner-Take-All Circuit with Controllable Soft Max Property.
An Oculo-Motor System with Multi-Chip Neuromorphic Analog VLSI Control.
The Parallel Problems Server: an Interactive Tool for Large Scale Machine Learning.
Manifold Stochastic Dynamics for Bayesian Learning.
Data Visualization and Feature Selection: New Algorithms for Nongaussian Data.
A MCMC Approach to Hierarchical Mixture Modelling.
Correctness of Belief Propagation in Gaussian Graphical Models of Arbitrary Topology.
Dual Estimation and the Unscented Transformation.
Support Vector Method for Multivariate Density Estimation.
The Relevance Vector Machine.
Building Predictive Models from Fractal Representations of Symbolic Sequences.
On Input Selection with Reversible Jump Markov Chain Monte Carlo Sampling.
Predictive App roaches for Choosing Hyperparameters in Gaussian Processes.
Training Data Selection for Optimal Generalization in Trigonometric Polynomial Networks.
Agglomerative Information Bottleneck.
Leveraged Vector Machines.
Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers.
Greedy Importance Sampling.
Better Generative Models for Sequential Data Problems: Bidirectional Recurrent Mixture Density Networks.
Support Vector Method for Novelty Detection.
An Analysis of Turbo Decoding with Gaussian Densities.
Nonlinear Discriminant Analysis Using Kernel Functions.
v-Arc: Ensemble Learning in the Presence of Outliers.
The Infinite Gaussian Mixture Model.
Large Margin DAGs for Multiclass Classification.
Optimal Kernel Shapes for Local Linear Regression.
Approximate Inference A lgorithms for Two-Layer Bayesian Networks.
Invariant Feature Extraction and Classification in Kernel Spaces.
A Multi-class Linear Learning Algorithm Related to Winnow.
Boosting Algorithms as Gradient Descent.
Bayesian Network Induction via Local Neighborhoods.
The Relaxed Online Maximum Margin Algorithm.
Algorithms for Independent Components Analysis and Higher Order Statistics.
An Improved Decomposition Algorithm for Regression Support Vector Machines.
Topographic Transformation as a Discrete Latent Variable.
Maximum Entropy Discrimination.
Learning to Parse Images.
Bayesian Transduction.
Variational Inference for Bayesian Mixtures of Factor Analysers.
Local Probability Propagation for Factor Analysis.
Differentiating Functions of the Jacobian with Respect to the Weights.
The Nonnegative Boltzmann Machine.
Transductive Inference for Estimating Values of Functions.
Reconstruction of Sequential Data with Probabilistic Models and Continuity Constraints.
Robust Neural Network Regression for Offline and Online Learning.
Modeling High-Dimensional Discrete Data with Multi-Layer Neural Networks.
Gaussian Fields for Approximate Inference in Layered Sigmoid Belief Networks.
Independent Factor Analysis with Temporally Structured Sources.
Robust Full Bayesian Methods for Neural Networks.
Some Theoretical Results Concerning the Convergence of Compositions of Regularized Linear Functions.
Semiparametric Approach to Multichannel Blind Deconvolution of Nonminimum Phase Systems.
Algebraic Analysis for Non-regular Learning Machines.
Probabilistic Methods for Support Vector Machines.
The Entropy Regularization Information Criterion.
Noisy Neural Networks and Generalizations.
Lower Bounds on the Complexity of Approximating Continuous Functions by Sigmoidal Neural Networks.
Understanding Stepwise Generalization of Support Vector Machines: a Toy Model.
Resonance in a Stochastic Neuron Model with Delayed Interaction.
Inference for the Generalization Error.
Boosting with Multi-Way Branching in Decision Trees.
Neural Computation with Winner-Take-All as the Only Nonlinear Operation.
Statistical Dynamics of Batch Learning.
Mixture Density Estimation.
Regular and Irregular Gallager-zype Error-Correcting Codes.
Bayesian Averaging is Well-Temperated.
Potential Boosters?
Efficient Approaches to Gaussian Process Classification.
A Geometric Interpretation of v-SVM Classifiers.
Dynamics of Supervised Learning with Restricted Training Sets and Noisy Teachers.
Model Selection for Support Vector Machines.
Uniqueness of the SVM Solution.
Model Selection in Clustering by Uniform Convergence Bounds.
A Variational Baysian Framework for Graphical Models.
Spike-based Learning Rules and Stabilization of Persistent Neural Activity.
Population Decoding Based on an Unfaithful Model.
An MEG Study of Response Latency and Variability in the Human Visual System During a Visual-Motor Integration Task.
Information Capacity and Robustness of Stochastic Neuron Models.
A Recurrent Model of the Interaction Between Prefrontal and Inferotemporal Cortex in Delay Tasks.
Predictive Sequence Learning in Recurrent Neocortical Circuits.
Memory Capacity of Linear vs. Nonlinear Models of Dendritic Integration.
LTD Facilitates Learning in a Noisy Environment.
Channel Noise in Excitable Neural Membranes.
Can VI Mechanisms Account for Figure-Ground and Medial Axis Effects?
Distributed Synchrony of Spiking Neurons in a Hebbian Cell Assembly.
Spiking Boltzmann Machines.
Neural Representation of Multi-Dimensional Stimuli.
Optimal Sizes of Dendritic and Axonal Arbors.
Wiring Optimization in the Brain.
Effective Learning Requires Neuronal Remodeling of Hebbian Synapses.
Recurrent Cortical Competition: Strengthen or Weaken?
A Generative Model for Attractor Dynamics.
Learning Statistically Neutral Tasks without Expert Guidance.
Evolving Learnable Languages.
Rules and Similarity in Concept Learning.
Graded Grammaticality in Prediction Fractal Machines.
Information Factorization in Connectionist Models of Perception.
Perceptual Organization Based on Temporal Dynamics.
Robust Recognition of Noisy and Superimposed Patterns via Selective Attention.
Acquisition in Autoshaping.
Effects of Spatial and Temporal Contiguity on the Acquisition of Spatial Information.
A Neurodynamical Approach to Visual Attention.
Recognizing Evoked Potentials in a Virtual Environment.
