NeurIPS(NIPS) 1999 论文列表Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30 - December 5, 1998]. |
Experimental Results on Learning Stochastic Memoryless Policies for Partially Observable Markov Decision Processes.
Improved Switching among Temporally Abstract Actions.
A Reinforcement Learning Algorithm in Partially Observable Environments Using Short-Term Memory.
Reinforcement Learning Based on On-Line EM Algorithm.
Learning Macro-Actions in Reinforcement Learning.
Coordinate Transformation Learning of Hand Position Feedback Controller by Using Change of Position Error Norm.
Risk Sensitive Reinforcement Learning.
Barycentric Interpolators for Continuous Space and Time Reinforcement Learning.
Learning Instance-Independent Value Functions to Enhance Local Search.
The Effect of Eligibility Traces on Finding Optimal Memoryless Policies in Partially Observable Markov Decision Processes.
Exploring Unknown Environments with Real-Time Search or Reinforcement Learning.
Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms.
Viewing Classifier Systems as Model Free Learning in POMDPs.
Optimizing Admission Control while Ensuring Quality of Service in Multimedia Networks via Reinforcement Learning.
Non-Linear PI Control Inspired by Biological Control Systems.
Gradient Descent for General Reinforcement Learning.
Robust, Efficient, Globally-Optimized Reinforcement Learning with the Parti-Game Algorithm.
Using Collective Intelligence to Route Internet Traffic.
Robot Docking Using Mixtures of Gaussians.
Applications of Multi-Resolution Neural Networks to Mammography.
Independent Component Analysis of Intracellular Calcium Spike Data.
Graphical Models for Recognizing Human Interactions.
Reinforcement Learning for Trading.
Bayesian Modeling of Facial Similarity.
Scheduling Straight-Line Code Using Reinforcement Learning and Rollouts.
Graph Matching for Shape Retrieval.
Call-Based Fraud Detection in Mobile Communication Networks Using a Hierarchical Regime-Switching Model.
Fast Neural Network Emulation of Dynamical Systems for Computer Animation.
Familiarity Discrimination of Radar Pulses.
Vertex Identification in High Energy Physics Experiments.
Adding Constrained Discontinuities to Gaussian Process Models of Wind Fields.
Probabilistic Modeling for Face Orientation Discrimination: Learning from Labeled and Unlabeled Data.
Making Templates Rotationally Invariant. An Application to Rotated Digit Recognition.
Classification in Non-Metric Spaces.
Orientation, Scale, and Discontinuity as Emergent Properties of Illusory Contour Shape.
Probabilistic Image Sensor Fusion.
General-Purpose Localization of Textured Image Regions.
Learning Lie Groups for Invariant Visual Perception.
Support Vector Machines Applied to Face Recognition.
A V1 Model of Pop Out and Asymmetty in Visual Search.
Attentional Modulation of Human Pattern Discrimination Psychophysics Reproduced by a Quantitative Model.
Learning to Find Pictures of People.
Learning to Estimate Scenes from Images.
Example-Based Image Synthesis of Articulated Figures.
A Phase Space Approach to Minimax Entropy Learning and the Minutemax Approximations.
Markov Processes on Curves for Automatic Speech Recognition.
Maximum-Likelihood Continuity Mapping (MALCOM): An Alternative to HMMs.
Controlling the Complexity of HMM Systems by Regularization.
Coding Time-Varying Signals Using Sparse, Shift-Invariant Representations.
An Entropic Estimator for Structure Discovery.
A High Performance k-NN Classifier Using a Binary Correlation Matrix Memory.
Computation of Smooth Optical Flow in a Feedback Connected Analog Network.
An Integrated Vision Sensor for the Computation of Optical Flow Singular Points.
A Neuromorphic Monaural Sound Localizer.
VLSI Implementation of Motion Centroid Localization for Autonomous Navigation.
Optimizing Correlation Algorithms for Hardware-Based Transient Classification.
A Micropower CMOS Adaptive Amplitude and Shift Invariant Vector Quantiser.
Active Noise Canceling Using Analog Neuro-Chip with On-Chip Learning Capability.
Analog VLSI Cellular Implementation of the Boundary Contour System.
Blind Separation of Filtered Sources Using State-Space Approach.
Convergence Rates of Algorithms for Visual Search: Detecting Visual Contours.
DTs: Dynamic Trees.
Basis Selection for Wavelet Regression.
The Bias-Variance Tradeoff and the Randomized GACV.
Discovering Hidden Features with Gaussian Processes Regression.
Learning Mixture Hierarchies.
SMEM Algorithm for Mixture Models.
Probabilistic Visualisation of High-Dimensional Binary Data.
Semiparametric Support Vector and Linear Programming Machines.
Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint Entropy.
Boxlets: A Fast Convolution Algorithm for Signal Processing and Neural Networks.
Regularizing AdaBoost.
Using Analytic QP and Sparseness to Speed Training of Support Vector Machines.
Replicator Equations, Maximal Cliques, and Graph Isomorphism.
Very Fast EM-Based Mixture Model Clustering Using Multiresolution Kd-Trees.
Kernel PCA and De-Noising in Feature Spaces.
Sebastian Mika Bernhard Schölkopf Alexander J. Smola Klaus-Robert Müller Matthias Scholz Gunnar Rätsch
Exploratory Data Analysis Using Radial Basis Function Latent Variable Models.
Neural Networks for Density Estimation.
Learning a Continuous Hidden Variable Model for Binary Data.
Unsupervised Classification with Non-Gaussian Mixture Models Using ICA.
A Polygonal Line Algorithm for Constructing Principal Curves.
Maximum Conditional Likelihood via Bound Maximization and the CEM Algorithm.
Exploiting Generative Models in Discriminative Classifiers.
Restructuring Sparse High Dimensional Data for Effective Retrieval.
Sparse Code Shrinkage: Denoising by Nonlinear Maximum Likelihood Estimation.
Learning from Dyadic Data.
Source Separation as a By-Product of Regularization.
Visualizing Group Structure.
Outcomes of the Equivalence of Adaptive Ridge with Least Absolute Shrinkage.
Classification on Pairwise Proximity Data.
Learning Nonlinear Dynamical Systems Using an EM Algorithm.
A Randomized Algorithm for Pairwise Clustering.
Efficient Bayesian Parameter Estimation in Large Discrete Domains.
Global Optimisation of Neural Network Models via Sequential Sampling.
Fisher Scoring and a Mixture of Modes Approach for Approximate Inference and Learning in Nonlinear State Space Models.
Approximate Learning of Dynamic Models.
Learning Multi-Class Dynamics.
Bayesian PCA.
Lazy Learning Meets the Recursive Least Squares Algorithm.
Semi-Supervised Support Vector Machines.
Learning a Hierarchical Belief Network of Independent Factor Analyzers.
A Theory of Mean Field Approximation.
Learning Curves for Gaussian Processes.
Discontinuous Recall Transitions Induced by Competition Between Short- and Long-Range Interactions in Recurrent Networks.
Shrinking the Tube: A New Support Vector Regression Algorithm.
Tight Bounds for the VC-Dimension of Piecewise Polynomial Networks.
On-Line Learning with Restricted Training Sets: Exact Solution as Benchmark for General Theories.
Mean Field Methods for Classification with Gaussian Processes.
General Bounds on Bayes Errors for Regression with Gaussian Processes.
On the Optimality of Incremental Neural Network Algorithms.
Direct Optimization of Margins Improves Generalization in Combined Classifiers.
A Precise Characterization of the Class of Languages Recognized by Neural Nets under Gaussian and Other Common Noise Distributions.
Computational Differences between Asymmetrical and Symmetrical Networks.
Stationarity and Stability of Autoregressive Neural Network Processes.
Inference in Multilayer Networks via Large Deviation Bounds.
Optimizing Classifers for Imbalanced Training Sets.
The Belief in TAP.
Convergence of the Wake-Sleep Algorithm.
Unsupervised and Supervised Clustering: The Mutual Information between Parameters and Observations.
Linear Hinge Loss and Average Margin.
Finite-Dimensional Approximation of Gaussian Processes.
Phase Diagram and Storage Capacity of Sequence-Storing Neural Networks.
Dynamically Adapting Kernels in Support Vector Machines.
Dynamics of Supervised Learning with Restricted Training Sets.
Almost Linear VC Dimension Bounds for Piecewise Polynomial Networks.
Tractable Variational Structures for Approximating Graphical Models.
Distributional Population Codes and Multiple Motion Models.
The Effect of Correlations on the Fisher Information of Population Codes.
Information Maximization in Single Neurons.
Modeling Surround Suppression in V1 Neurons with a Statistically Derived Normalization Model.
Multi-Electrode Spike Sorting by Clustering Transfer Functions.
The Role of Lateral Cortical Competition in Ocular Dominance Development.
Signal Detection in Noisy Weakly-Active Dendrites.
Spike-Based Compared to Rate-Based Hebbian Learning.
Analyzing and Visualizing Single-Trial Event-Related Potentials.
Tzyy-Ping Jung Scott Makeig Marissa Westerfield Jeanne Townsend Eric Courchesne Terrence J. Sejnowski
Synergy and Redundancy among Brain Cells of Behaving Monkeys.
Divisive Normalization, Line Attractor Networks and Ideal Observers.
Neuronal Regulation Implements Efficient Synaptic Pruning.
Recurrent Cortical Amplification Produces Complex Cell Responses.
Where Does the Population Vector of Motor Cortical Cells Point during Reaching Movements?
Contrast Adaptation in Simple Cells by Changing the Transmitter Release Probability.
Temporally Asymmetric Hebbian Learning, Spike liming and Neural Response Variability.
Bayesian Modeling of Human Concept Learning.
A Principle for Unsupervised Hierarchical Decomposition of Visual Scenes.
Mechanisms of Generalization in Perceptual Learning.
Utilizing lime: Asynchronous Binding.
Multiple Paired Forward-Inverse Models for Human Motor Learning and Control.
Facial Memory Is Kernel Density Estimation (Almost).
A Model for Associative Multiplication.
Perceiving without Learning: From Spirals to Inside/Outside Relations.
Evidence for a Forward Dynamics Model in Human Adaptive Motor Control.