NeurIPS(NIPS) 1993 论文列表Advances in Neural Information Processing Systems 5, [NIPS Conference, Denver, Colorado, USA, November 30 - December 3, 1992]. |
An Information-Theoretic Approach to Deciphering the Hippocampal Code.
A Formal Model of the Insect Olfactory Macroglomerulus: Simulations and Analytic Results.
A Recurrent Neural Network for Generation of Occular Saccades.
Parametrizing Feature Sensitive Cell Formation in Linsker Networks in the Auditory System.
Spiral Waves in Integrate-and-Fire Neural Networks.
Statistical and Dynamical Interpretation of ISIH Data from Periodically Stimulated Sensory Neurons.
Topography and Ocular Dominance with Positive Correlations.
How Oscillatory Neuronal Responses Reflect Bistability and Switching of the Hidden Assembly Dynamics.
Using Aperiodic Reinforcement for Directed Self-Organization During Development.
Biologically Plausible Local Learning Rules for the Adaptation of the Vestibulo-Ocular Reflex.
Deriving Receptive Fields Using an Optimal Encoding Criterion.
Statistical Modeling of Cell Assemblies Activities in Associative Cortex of Behaving Monkeys.
Adaptive Stimulus Representations: A Computational Theory of Hippocampal-Region Functions.
Using Hippocampal 'Plane Cells' for Navigation, Exploiting Phase Coding.
A Neural Model of Descending Gain Control in the Electrosensory System.
Maaping Between Neural and Physical Activities of the Lobster Gastric Mill.
Perceiving Complex Visual Scenes: An Oscillator Neural Network Model that Integrates Selective Attention, Perceptual Organization, and Invariant Recognition.
Word Space.
A Knowledge-Based Model of Geometry Learning.
A Dynamical Model of Priming and Repetition Blindness.
Network Structuring and Training Using Rule-Based Knowledge.
A Connectionist Symbol Manipulator that Discovers the Structure of Context-Free Languages.
Analogy - Watershed or Waterloo? Structural Alignment and the Development of Connectionist Models of Cognition.
Harmonic Grammars for Formal Languages.
A Parallel Gradient Descent Method for Learning in Analog VLSI Neural Networks.
Object-Based Analog VLSI Vision Circuits.
Silicon Auditory Processors as Computer Peripherals.
Hybrid Circuits of Interacting Computer Model and Biological Neurons.
Attractor Neural Networks with Local Inhibition: From Statistical Physics to a Digitial Programmable Integrated Circuit.
An Object-Oriented Framework for the Simulation of Neural Networks.
Analog VLSI Implementation of Gradient Descent.
Visual Motion Computation in Analog VLSI Using Pulses.
Generic Analog Neural Computation - The Epsilon Chip.
An Analog VLSI Chip for Radial Basis Functions.
A Neural Network that Learns to Interpret Myocardial Planar Thallium Scintigrams.
Hidden Markov Models in Molecular Biology: New Algorithms and Applications.
Forecasting Demand for Electric Power.
Planar Hidden Markov Modeling: From Speech to Optical Character Recognition.
Recognition-Based Segmentation of On-Line Hand-Printed Words.
Connected Letter Recognition with a Multi-State Time Delay Neural Network.
A Hybrid Neural Net System for State-of-the-Art Continuous Speech Recognition.
Performance Through Consistency: MS-TDNN's for Large Vocabulary Continuous Speech Recognition.
Transient Signal Detection with Neural Networks: The Search for the Desired Signal.
Modeling Consistency in a Speaker Independent Continuous Speech Recognition System.
A Hybrid Linear/Nonlinear Approach to Channel Equalization Problems.
Analog Cochlear Model for Multiresolution Speech Analysis.
Physiologically Based Speech Synthesis.
Context-Dependent Multiple Distribution Phonetic Modeling with MLPs.
Some Estimates on the Number of Connections and Hidden Units for Feed-Forward Networks.
Learning Cellular Automation Dynamics with Neural Networks.
Rational Parametrizations of Neural Networks.
The Power of Approximation: A Comparison of Activation Functions.
Learning Curves, Model Selection and Complexity of Neural Networks.
Neural Network Model Selection Using Asymptotic Jackknife Estimator and Cross-Validation Method.
On Learning µ-Perceptron Networks with Binary Weights.
Non-Linear Dimensionality Reduction.
History-Dependent Attractor Neural Networks.
Single-Iteration Threshold Hamming Networks.
Predicting Complex Behavior in Sparse Asymmetric Networks.
Destabilization and Route to Chaos in Neural Networks with Random Connectivity.
On the Use of Evidence in Neural Networks.
Probability Estimator from a Database Using a Gibbs Energy Model.
Statistical Mechanics of Learning in a Large Committee Machine.
Information Theoretic Analysis of Connection Structure from Spike Trains.
Weight Space Probability Densities in Stochastic Learning: II. Transients and Basin Hopping Times.
Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain.
Synaptic Weight Noise During MLP Learning Enhances Fault-Tolerance, Generalization and Learning Trajectory.
Information, Prediction, and Query by Committee.
Bayesian Learning via Stochastic Dynamics.
Self-Organizing Rules for Robust Principal Component Analysis.
Diffusion Approximations for the Constant Step Size Backpropagation Algorithm and Resistance to Local Minima.
Weight Space Probability Densities in Stochastic Learning: I. Dynamics and Equilibria.
Learning to See Where and What: Training a Net to Make Saccades and Recognize Handwritten Characters.
Computation of Heading Direction from Optic Flow in Visual Cortex.
Remote Sensing Image Analysis via a Texture Classification Neural Network.
Unsmearing Visual Motion: Development of Long-Range Horizontal Intrinsic Connections.
A Model of Feedback to the Lateral Geniculate Nucleus.
Improving Convergence in Hierarchical Matching Networks for Object Recognition.
Some Solutions to the Missing Feature Problem in Vision.
The Computation of Stereo Disparity for Transparent and for Opaque Surfaces.
Stimulus Encoding by Multidimensional Receptive Fields in Single Cells and Cell Populations in V1 of Awake Monkey.
Filter Selection Model for Generating Visual Motion Signals.
Learning to Categorize Objects Using Temporal Coherence.
Learning Fuzzy Rule-Based Neural Networks for Control.
Learning Spatio-Temporal Planning from a Dynamic Programming Teacher: Feed-Forward Neurocontrol for Moving Obstacle Avoidance.
A Practice Strategy for Robot Learning Control.
Learning Control Under Extreme Uncertainty.
On Line Estimation of Optimal Control Sequences: HJB Estimators.
Integration of Visual and Somatosensory Information for Preshaping Hand in Grasping Movements.
Neural Network On-Line Learning Control of Spacecraft Smart Structures.
Reinforcement Learning Applied to Linear Quadratic Regulation.
Explanation-Based Neural Network Learning for Robot Control.
Input Reconstruction Reliability Estimation.
Feudal Reinforcement Learning.
Memory-Based Reinforcement Learning: Efficient Computation with Prioritized Sweeping.
Global Regularization of Inverse Kinematics for Redundant Manipulators.
A Fast Stochastic Error-Descent Algorithm for Supervised Learning and Optimization.
Synchronization and Grammatical Inference in an Oscillating Elman Net.
Extended Regularization Methods for Nonconvergent Model Selection.
A Note on Learning Vector Quantization.
Summed Weight Neuron Perturbation: An O(N) Improvement Over Weight Perturbation.
Discriminability-Based Transfer between Neural Networks.
Assessing and Improving Neural Network Predictions by the Bootstrap Algorithm.
Generalization Abilities of Cascade Network Architecture.
Time Warping Invariant Neural Networks.
Directional-Unit Boltzmann Machines.
Second Order Derivatives for Network Pruning: Optimal Brain Surgeon.
Automatic Learning Rate Maximization in Large Adaptive Machines.
Automatic Capacity Tuning of Very Large VC-Dimension Classifiers.
A Boundary Hunting Radial Basis Function Classifier which Allocates Centers Constructively.
Metamorphosis Networks: An Alternative to Constructive Models.
Kohonen Feature Maps and Growing Cell Structures - a Performance Comparison.
Learning Sequential Tasks by Incrementally Adding Higher Orders.
Combining Neural and Symbolic Learning to Revise Probabilistic Rule Bases.
Interposing an Ontogenetic Model Between Genetic Algorithms and Neural Networks.
Nets with Unreliable Hidden Nodes Learn Error-Correcting Codes.
Q-Learning with Hidden-Unit Restarting.
A Method for Learning From Hints.
Using Prior Knowledge in a {NNPDA} to Learn Context-Free Languages.
Optimal Depth Neural Networks for Multiplication and Related Problems.
Efficient Pattern Recognition Using a New Transformation Distance.
Improving Performance in Neural Networks Using a Boosting Algorithm.
Holographic Recurrent Networks.
Intersecting Regions: The Key to Combinatorial Structure in Hidden Unit Space.
Computing with Almost Optimal Size Neural Networks.
Hidden Markov Model} Induction by Bayesian Model Merging.
On the Use of Projection Pursuit Constraints for Training Neural Networks.