NeurIPS(NIPS) 2001 论文列表Advances in Neural Information Processing Systems 13, Papers from Neural Information Processing Systems (NIPS) 2000, Denver, CO, USA. |
APRICODD: Approximate Policy Construction Using Decision Diagrams.
Balancing Multiple Sources of Reward in Reinforcement Learning.
Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning Task.
Kernel-Based Reinforcement Learning in Average-Cost Problems: An Application to Optimal Portfolio Choice.
Robust Reinforcement Learning.
Automated State Abstraction for Options using the U-Tree Algorithm.
Hierarchical Memory-Based Reinforcement Learning.
Reinforcement Learning with Function Approximation Converges to a Region.
Decomposition of Reinforcement Learning for Admission Control of Self-Similar Call Arrival Processes.
Exact Solutions to Time-Dependent MDPs.
Programmable Reinforcement Learning Agents.
Bayesian Video Shot Segmentation.
Machine Learning for Video-Based Rendering.
The Use of Classifiers in Sequential Inference.
Bayes Networks on Ice: Robotic Search for Antarctic Meteorites.
Learning Switching Linear Models of Human Motion.
Interactive Parts Model: An Application to Recognition of On-line Cursive Script.
Probabilistic Semantic Video Indexing.
Sex with Support Vector Machines.
Recognizing Hand-written Digits Using Hierarchical Products of Experts.
Support Vector Novelty Detection Applied to Jet Engine Vibration Spectra.
A Comparison of Image Processing Techniques for Visual Speech Recognition Applications.
A Neural Probabilistic Language Model.
From Mixtures of Mixtures to Adaptive Transform Coding.
Divisive and Subtractive Mask Effects: Linking Psychophysics and Biophysics.
Rate-coded Restricted Boltzmann Machines for Face Recognition.
Redundancy and Dimensionality Reduction in Sparse-Distributed Representations of Natural Objects in Terms of Their Local Features.
Learning and Tracking Cyclic Human Motion.
Learning Sparse Image Codes using a Wavelet Pyramid Architecture.
Partially Observable SDE Models for Image Sequence Recognition Tasks.
Learning Segmentation by Random Walks.
Color Opponency Constitutes a Sparse Representation for the Chromatic Structure of Natural Scenes.
Keeping Flexible Active Contours on Track using Metropolis Updates.
Feature Correspondence: A Markov Chain Monte Carlo Approach.
The Manhattan World Assumption: Regularities in Scene Statistics which Enable Bayesian Inference.
Emergence of Movement Sensitive Neurons' Properties by Learning a Sparse Code for Natural Moving Images.
Shape Context: A New Descriptor for Shape Matching and Object Recognition.
Noise Suppression Based on Neurophysiologically-motivated SNR Estimation for Robust Speech Recognition.
FaceSync: A Linear Operator for Measuring Synchronization of Video Facial Images and Audio Tracks.
Periodic Component Analysis: An Eigenvalue Method for Representing Periodic Structure in Speech.
Minimum Bayes Error Feature Selection for Continuous Speech Recognition.
One Microphone Source Separation.
Higher-Order Statistical Properties Arising from the Non-Stationarity of Natural Signals.
Factored Semi-Tied Covariance Matrices.
Learning Joint Statistical Models for Audio-Visual Fusion and Segregation.
Combining ICA and Top-Down Attention for Robust Speech Recognition.
Speech Denoising and Dereverberation Using Probabilistic Models.
New Approaches Towards Robust, Adaptive Speech Recognition (invited paper).
Four-legged Walking Gait Control Using a Neuromorphic Chip Interfaced to a Support Vector Learning Algorithm.
Fast Training of Support Vector Classifiers.
Homeostasis in a Silicon Integrate and Fire Neuron.
Smart Vision Chip Fabricated Using Three Dimensional Integration Technology.
Hiroyuki Kurino Masaki Nakagawa Kang Wook Lee Tomonori Nakamura Yuusuke Yamada Ki Tae Park Mitsumasa Koyanagi
A Silicon Primitive for Competitive Learning.
Regularized Winnow Methods.
A Gradient-Based Boosting Algorithm for Regression Problems.
Generalized Belief Propagation.
Using the Nyström Method to Speed Up Kernel Machines.
On a Connection between Kernel PCA and Metric Multidimensional Scaling.
Feature Selection for SVMs.
Tree-Based Modeling and Estimation of Gaussian Processes on Graphs with Cycles.
Mixtures of Gaussian Processes.
Active Learning for Parameter Estimation in Bayesian Networks.
Data Clustering by Markovian Relaxation and the Information Bottleneck Method.
Sparse Kernel Principal Component Analysis.
Kernel Expansions with Unlabeled Examples.
Sparse Greedy Gaussian Process Regression.
An Information Maximization Approach to Overcomplete and Recurrent Representations.
On Iterative Krylov-Dogleg Trust-Region Steps for Solving Neural Networks Nonlinear Least Squares Problems.
Automatic Choice of Dimensionality for PCA.
A Mathematical Programming Approach to the Kernel Fisher Algorithm.
The Unscented Particle Filter.
Active Support Vector Machine Classification.
Constrained Independent Component Analysis.
Text Classification using String Kernels.
Algorithms for Non-negative Matrix Factorization.
Generalizable Singular Value Decomposition for Ill-posed Datasets.
Ensemble Learning and Linear Response Theory for ICA.
Beyond Maximum Likelihood and Density Estimation: A Sample-Based Criterion for Unsupervised Learning of Complex Models.
Large Scale Bayes Point Machines.
'N-Body' Problems in Statistical Learning.
The Kernel Gibbs Sampler.
Propagation Algorithms for Variational Bayesian Learning.
A New Approximate Maximal Margin Classification Algorithm.
Sequentially Fitting "Inclusive" Trees for Inference in Noisy-OR Networks.
Accumulator Networks: Suitors of Local Probability Propagation.
Discovering Hidden Variables: A Structure-Based Approach.
Incorporating Second-Order Functional Knowledge for Better Option Pricing.
High-temperature Expansions for Learning Models of Nonnegative Data.
An Adaptive Metric Machine for Pattern Classification.
Explaining Away in Weight Space.
Sparse Representation for Gaussian Process Models.
Improved Output Coding for Classification Using Continuous Relaxation.
The Missing Link - A Probabilistic Model of Document Content and Hypertext Connectivity.
Gaussianization.
Vicinal Risk Minimization.
Incremental and Decremental Support Vector Machine Learning.
Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping.
A Linear Programming Approach to Novelty Detection.
Model Complexity, Goodness of Fit and Diminishing Returns.
Direct Classification with Indirect Data.
A Variational Mean-Field Theory for Sigmoidal Belief Networks.
A Support Vector Method for Clustering.
Convergence of Large Margin Separable Linear Classification.
Learning Winner-take-all Competition Between Groups of Neurons in Lateral Inhibitory Networks.
Stagewise Processing in Error-correcting Codes and Image Restoration.
Computing with Finite and Infinite Networks.
Algebraic Information Geometry for Learning Machines with Singularities.
Error-correcting Codes on a Bethe-like Lattice.
Analysis of Bit Error Probability of Direct-Sequence CDMA Multiuser Demodulators.
Regularization with Dot-Product Kernels.
The Kernel Trick for Distances.
Occam's Razor.
Learning Continuous Distributions: Simulations With Field Theoretic Priors.
Weak Learners and Improved Rates of Convergence in Boosting.
Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations.
A Tighter Bound for Graphical Models.
Foundations for a Circuit Complexity Theory of Sensory Processing.
Sparsity of Data Representation of Optimal Kernel Machine and Leave-one-out Estimator.
Some New Bounds on the Generalization Error of Combined Classifiers.
Second Order Approximations for Probability Models.
On Reversing Jensen's Inequality.
A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work.
Permitted and Forbidden Sets in Symmetric Threshold-Linear Networks.
From Margin to Sparsity.
Competition and Arbors in Ocular Dominance.
Algorithmic Stability and Generalization Performance.
Efficient Learning of Linear Perceptrons.
Whence Sparseness?
Development of Hybrid Systems: Interfacing a Silicon Neuron to a Leech Heart Interneuron.
Mario F. Simoni Gennady S. Cymbalyuk Michael Elliott Sorensen Ronald L. Calabrese Stephen P. DeWeerth
Natural Sound Statistics and Divisive Normalization in the Auditory System.
Universality and Individuality in a Neural Code.
Spike-Timing-Dependent Learning for Oscillatory Networks.
Processing of Time Series by Neural Circuits with Biologically Realistic Synaptic Dynamics.
Finding the Key to a Synapse.
Dopamine Bonuses.
Multiple Timescales of Adaptation in a Neural Code.
A New Model of Spatial Representation in Multimodal Brain Areas.
Temporally Dependent Plasticity: An Information Theoretic Account.
Stability and Noise in Biochemical Switches.
Modelling Spatial Recall, Mental Imagery and Neglect.
Place Cells and Spatial Navigation Based on 2D Visual Feature Extraction, Path Integration, and Reinforcement Learning.
Dendritic Compartmentalization Could Underlie Competition and Attentional Biasing of Simultaneous Visual Stimuli.
What Can a Single Neuron Compute?
Adaptive Object Representation with Hierarchically-Distributed Memory Sites.
Structure Learning in Human Causal Induction.
The Early Word Catches the Weights.
Active Inference in Concept Learning.
The Use of MDL to Select among Computational Models of Cognition.
Position Variance, Recurrence and Perceptual Learning.
Hippocampally-Dependent Consolidation in a Hierarchical Model of Neocortex.
The Interplay of Symbolic and Subsymbolic Processes in Anagram Problem Solving.
A Productive, Systematic Framework for the Representation of Visual Structure.
Who Does What? A Novel Algorithm to Determine Function Localization.