NeurIPS(NIPS) 1997 论文列表Advances in Neural Information Processing Systems 9, NIPS, Denver, CO, USA, December 2-5, 1996. |
Approximate Solutions to Optimal Stopping Problems.
Analysis of Temporal-Diffference Learning with Function Approximation.
On-line Policy Improvement using Monte-Carlo Search.
Learning Decision Theoretic Utilities through Reinforcement Learning.
Analytical Mean Squared Error Curves in Temporal Difference Learning.
Exploiting Model Uncertainty Estimates for Safe Dynamic Control Learning.
Learning from Demonstration.
Multi-Grid Methods for Reinforcement Learning in Controlled Diffusion Processes.
Reinforcement Learning for Mixed Open-loop and Closed-loop Control.
Local Bandit Approximation for Optimal Learning Problems.
Efficient Nonlinear Control with Actor-Tutor Architecture.
Multidimensional Triangulation and Interpolation for Reinforcement Learning.
Multi-effect Decompositions for Financial Data Modeling.
Interpolating Earth-science Data using RBF Networks and Mixtures of Experts.
Spectroscopic Detection of Cervical Pre-Cancer through Radial Basis Function Networks.
Reinforcement Learning for Dynamic Channel Allocation in Cellular Telephone Systems.
A Comparison between Neural Networks and other Statistical Techniques for Modeling the Relationship between Tobacco and Alcohol and Cancer.
Sequential Tracking in Pricing Financial Options using Model Based and Neural Network Approaches.
The Neurothermostat: Predictive Optimal Control of Residential Heating Systems.
Multi-Task Learning for Stock Selection.
Predicting Lifetimes in Dynamically Allocated Memory.
Adaptive Access Control Applied to Ethernet Data.
An Orientation Selective Neural Network for Pattern Identification in Particle Detectors.
Salient Contour Extraction by Temporal Binding in a Cortically-based Network.
Interpreting Images by Propagating Bayesian Beliefs.
Rapid Visual Processing using Spike Asynchrony.
Representing Face Images for Emotion Classification.
Visual Cortex Circuitry and Orientation Tuning.
Contour Organisation with the EM Algorithm.
ARTEX: A Self-organizing Architecture for Classifying Image Regions.
Selective Integration: A Model for Disparity Estimation.
Spatiotemporal Coupling and Scaling of Natural Images and Human Visual Sensitivities.
Learning Appearance Based Models: Mixtures of Second Moment Experts.
Compositionality, MDL Priors, and Object Recognition.
Edges are the Independent Components of Natural Scenes.
Learning Temporally Persistent Hierarchical Representations.
Viewpoint Invariant Face Recognition using Independent Component Analysis and Attractor Networks.
Effective Training of a Neural Network Character Classifier for Word Recognition.
Ensemble Methods for Phoneme Classification.
Dual Kalman Filtering Methods for Nonlinear Prediction, Smoothing and Estimation.
A Constructive Learning Algorithm for Discriminant Tangent Models.
Neural Network Modeling of Speech and Music Signals.
A New Approach to Hybrid HMM/ANN Speech Recognition using Mutual Information Neural Networks.
A Constructive RBF Network for Writer Adaptation.
Blind Separation of Delayed and Convolved Sources.
Dynamic Features for Visual Speechreading: A Systematic Comparison.
A Silicon Model of Amplitude Modulation Detection in the Auditory Brainstem.
Bangs, Clicks, Snaps, Thuds and Whacks: An Architecture for Acoustic Transient Processing.
A Micropower Analog VLSI HMM State Decoder for Wordspotting.
An Adaptive WTA using Floating Gate Technology.
Dynamically Adaptable CMOS Winner-Take-All Neural Network.
Analog VLSI Circuits for Attention-Based, Visual Tracking.
An Analog Implementation of the Constant Average Statistics Constraint For Sensor Calibration.
A Spike Based Learning Neuron in Analog VLSI.
VLSI Implementation of Cortical Visual Motion Detection Using an Analog Neural Computer.
Probabilistic Interpretation of Population Codes.
Early Brain Damage.
Separating Style and Content.
Fast Network Pruning and Feature Extraction by using the Unit-OBS Algorithm.
Clustering Sequences with Hidden Markov Models.
Training Algorithms for Hidden Markov Models using Entropy Based Distance Functions.
Monotonicity Hints.
Second-order Learning Algorithm with Squared Penalty Term.
A Convergence Proof for the Softassign Quadratic Assignment Algorithm.
Maximum Likelihood Blind Source Separation: A Context-Sensitive Generalization of ICA.
Using Curvature Information for Fast Stochastic Search.
Adaptive On-line Learning in Changing Environments.
Competition Among Networks Improves Committee Performance.
Smoothing Regularizers for Projective Basis Function Networks.
Learning Bayesian Belief Networks with Neural Network Estimators.
A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data.
Combining Neural Network Regression Estimates with Regularized Linear Weights.
Triangulation by Continuous Embedding.
Ordered Classes and Incomplete Examples in Classification.
NeuroScale: Novel Topographic Feature Extraction using RBF Networks.
Source Separation and Density Estimation by Faithful Equivariant SOM.
Bayesian Unsupervised Learning of Higher Order Structure.
ARC-LH: A New Adaptive Resampling Algorithm for Improving ANN Classifiers.
Unsupervised Learning by Convex and Conic Coding.
Unification of Information Maximization and Minimization.
Hidden Markov Decision Trees.
Combinations of Weak Classifiers.
Recursive Algorithms for Approximating Probabilities in Graphical Models.
One-unit Learning Rules for Independent Component Analysis.
LSTM can Solve Hard Long Time Lag Problems.
Balancing Between Bagging and Bumping.
Adaptively Growing Hierarchical Mixtures of Experts.
Continuous Sigmoidal Belief Networks Trained using Slice Sampling.
Limitations of Self-organizing Maps for Vector Quantization and Multidimensional Scaling.
Softening Discrete Relaxation.
On a Modification to the Mean Field EM Algorithm in Factorial Learning.
MIMIC: Finding Optima by Estimating Probability Densities.
Minimizing Statistical Bias with Queries.
488 Solutions to the XOR Problem.
Representation and Induction of Finite State Machines using Time-Delay Neural Networks.
Self-Organizing and Adaptive Algorithms for Generalized Eigen-Decomposition.
Promoting Poor Features to Supervisors: Some Inputs Work Better as Outputs.
Estimating Equivalent Kernels for Neural Networks: A Data Perturbation Approach.
Improving the Accuracy and Speed of Support Vector Machines.
Clustering via Concave Minimization.
The CONDENSATION Algorithm - Conditional Density Propagation and Applications to Visual Tracking.
GTM: A Principled Alternative to the Self-Organizing Map.
Regression with Input-Dependent Noise: A Bayesian Treatment.
Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo.
Bayesian Model Comparison by Monte Carlo Chaining.
Consistent Classification, Firm and Soft.
Genetic Algorithms and Explicit Search Statistics.
Time Series Prediction using Mixtures of Experts.
Microscopic Equations in Rough Energy Landscape for Neural Networks.
Computing with Infinite Networks.
The Learning Dynamcis of a Universal Approximator.
Support Vector Method for Function Approximation, Regression Estimation and Signal Processing.
Online Learning from Finite Training Sets: An Analytical Case Study.
A Variational Principle for Model-based Morphing.
Learning with Noise and Regularizers in Multilayer Neural Networks.
The Generalisation Cost of RAMnets.
Hebb Learning of Features based on their Information Content.
Are Hopfield Networks Faster than Conventional Computers?
Removing Noise in On-Line Search using Adaptive Batch Sizes.
A Mean Field Algorithm for Bayes Learning in Large Feed-forward Neural Networks.
On the Effect of Analog Noise in Discrete-Time Analog Computations.
Noisy Spiking Neurons with Temporal Coding have more Computational Power than Sigmoidal Neurons.
An Apobayesian Relative of Winnow.
Radial Basis Function Networks and Complexity Regularization in Function Learning.
MLP Can Provably Generalize Much Better than VC-bounds Indicate.
Statistical Mechanics of the Mixture of Experts.
Practical Confidence and Prediction Intervals.
The Effect of Correlated Input Data on the Dynamics of Learning.
Size of Multilayer Networks for Exact Learning: Analytic Approach.
Support Vector Regression Machines.
Multilayer Neural Networks: One or Two Hidden Layers?
Dynamics of Training.
For Valid Generalization the Size of the Weights is More Important than the Size of the Network.
Neural Learning in Structured Parameter Spaces - Natural Riemannian Gradient.
A Model of Recurrent Interactions in Primary Visual Cortex.
Cholinergic Modulation Preserves Spike Timing Under Physiologically Realistic Fluctuating Input.
An Architectural Mechanism for Direction-tuned Cortical Simple Cells: The Role of Mutual Inhibition.
Statistically Efficient Estimations Using Cortical Lateral Connections.
Orientation Contrast Sensitivity from Long-range Interactions in Visual Cortex.
Complex-Cell Responses Derived from Center-Surround Inputs: The Surprising Power of Intradendritic Computation.
Learning Exact Patterns of Quasi-synchronization among Spiking Neurons from Data on Multi-unit Recordings.
A Neural Model of Visual Contour Integration.
Extraction of Temporal Features in the Electrosensory System of Weakly Electric Fish.
Neural Network Models of Chemotaxis in the Nematode Caenorhabditis Elegans.
A Hierarchical Model of Visual Rivalry.
3D Object Recognition: A Model of View-Tuned Neurons.
Reconstructing Stimulus Velocity from Neuronal Responses in Area MT.
Temporal Low-Order Statistics of Natural Sounds.
Neural Models for Part-Whole Hierarchies.
Why did TD-Gammon Work?
Text-Based Information Retrieval Using Exponentiated Gradient Descent.