NeurIPS(NIPS) 1994 论文列表Advances in Neural Information Processing Systems 6, [7th NIPS Conference, Denver, Colorado, USA, 1993]. |
Putting It All Together: Methods for Combining Neural Networks.
Processing of Visual and Auditory Space and Its Modification by Experience.
Neural Network Models for Optimization Problems.
Learning in Computer Vision and Image Understanding.
Functional Models of Selective Attention and Context Dependency.
Connectionist Modeling and Parallel Architectures.
Catastrophic Interference in Connectionist Networks: Can It Be Predicted, Can It Be Prevented?
What Does the Hippocampus Compute?: A Precis of the 1993 NIPS Workshop.
Stability and Observability.
Robot Learning: Exploration and Continuous Domains.
Neurobiology, Psychophysics, and Computational Models of Visual Attention.
Memory-Based Methods for Regression and Classification.
Connectionism for Music and Audition.
Complexity Issues in Neural Computation and Learning.
Classification of Electroencephalogram Using Artificial Neural Networks.
Classification of Multi-Spectral Pixels by the Binary Diamond Neural Network.
Learning Mackey-Glass from 25 Examples, Plus or Minus 2.
Encoding Labeled Graphs by Labeling RAAM.
Analyzing Cross-Connected Networks.
Estimating Analogical Similarity by Dot-Products of Holographic Reduced Representations.
Emergence of Global Structure from Local Associations.
GDS: Gradient Descent Generation of Symbolic Classification Rules.
Tonal Music as a Componential Code: Learning Temporal Relationships between and within Pitch and Timing Components.
Computational Elements of the Adaptive Controller of the Human Arm.
Connectionist Models for Auditory Scene Analysis.
Segmental Neural Net Optimization for Continuous Speech Recognition.
Learning Temporal Dependencies in Connectionist Speech Recognition.
Inverse Dynamics of Speech Motor Control.
Speaker Recognition Using Neural Tree Networks.
Lipreading by Neural Networks: Visual Preprocessing, Learning, and Sensory Integration.
Figure of Merit Training for Detection and Spotting.
Analysis of Short Term Memories for Neural Networks.
Bayesian Self-Organization.
Dual Mechanisms for Neural Binding and Segmentation.
Two-Dimensional Object Localization by Coarse-to-Fine Correlation Matching.
Resolving Motion Ambiguities.
The Role of MT Neuron Receptive Field Surrounds in Computing Object Shape from Velocity Fields.
Feature Densities Are Required for Computing Feature Correspondences.
A Network Mechanism for the Determination of Shape-from-Texture.
Classifying Hand Gestures with a View-Based Distributed Representation.
Globally Trained Handwritten Word Recognizer Using Spatial Representation, Convolutional Neural Networks, and Hidden Markov Models.
Event-Driven Simulation of Networks of Spiking Neurons.
Implementing Intelligence on Silicon Using Neuron-Like Functional MOS Transistors.
Learning Complex Boolean Functions: Algorithms and Applications.
Efficient Simulation of Biological Neural Networks on Massively Parallel Supercomputers with Hypercube Architecture.
Digital Boltzmann VLSI for Constraint Satisfaction and Learning.
Michael Murray Ming-Tak Leung Kan Boonyanit Kong Kritayakirana James B. Burr Gregory J. Wolff Tokahiro Watanabe Edward L. Schwartz David G. Stork Allen M. Peterson
High Performance Neural Net Simulation on a Multiprocessor System with Intelligent Communication.
The Softmax Nonlinearity: Derivation Using Statistical Mechanics and Useful Properties as a Multiterminal Analog Circuit Element.
WATTLE: A Trainable Gain Analogue VLSI Neural Network.
VLSI Phase Locking Architectures for Feature Linking in Multiple Target Tracking Systems.
A Learning Analog Neural Network Chip with Continuous-Time Recurrent Dynamics.
A Hybrid Radial Basis Function Neurocomputer and Its Applications.
A Massively-Parallel {SIMD} Processor for Neural Network and Machine Vision Applications.
Decoding Cursive Scripts.
Probabilistic Anomaly Detection in Dynamic Systems.
Temporal Difference Learning of Position Evaluation in the Game of Go.
Neural Network Definition of Highly Predictable Protein Secondary Structure Classes.
Comparison Training for a Rescheduling Problem in Neural Networks.
Identifying Fault-Prone Software Modules Using Feed-Forward Networks: A Case Study.
Address Block Location with a Neural Net System.
Recognition-Based Segmentation of On-Line Cursive Handwriting.
Illumination-Invariant Face Recognition with a Contrast Sensitive Silicon Retina.
Hidden Markov Models for Human Genes.
Non-Intrusive Gaze Tracking Using Artificial Neural Networks.
Postal Address Block Location Using a Convolutional Locator Network.
Signature Verification Using a Siamese Time Delay Neural Network.
A Computational Model for Cursive Handwriting Based on the Minimization Principle.
Mixtures of Controllers for Jump Linear and Non-Linear Plants.
The Parti-Game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-Spaces.
Convergence of Stochastic Iterative Dynamic Programming Algorithms.
Convergence of Indirect Adaptive Asynchronous Value Iteration Algorithms.
Monte Carlo Matrix Inversion and Reinforcement Learning.
Neural Network Exploration Using Optimal Experiment Design.
Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach.
Using Local Trajectory Optimizers to Speed Up Global Optimization in Dynamic Programming.
Robust Reinforcement Learning in Motion Planning.
Exploiting Chaos to Control the Future.
Transition Point Dynamic Programming.
Synchronization, Oscillations and 1/f Noise in Networks of Spiking Neurons.
An Analog VLSI Model of Central Pattern Generation in the Leech.
Optimal Unsupervised Motor Learning Predicts the Internal Representation of Barn Owl Head Movements.
A Connectionist Model of the Owl's Sound Localization System.
Foraging in an Uncertain Environment Using Predictive Hebbian Learning.
Bayesian Modeling and Classification of Neural Signals.
An Analog VLSI Saccadic Eye Movement System.
Directional Hearing by the Mauthner System.
A Hodgkin-Huxley Type Neuron Model That Learns Slow Non-Spike Oscillations.
Dopaminergic Neuromodulation Brings a Dynamical Plasticity to the Retina.
Statistics of Natural Images: Scaling in the Woods.
Development of Orientation and Ocular Dominance Columns in Infant Macaques.
Lower Boundaries of Motoneuron Desynchronization via Renshaw Interneurons.
Odor Processing in the Bee: A Preliminary Study of the Role of Central Input to the Antennal Lobe.
Amplifying and Linearizing Apical Synaptic Inputs to Cortical Pyramidal Cells.
Dynamic Modulation of Neurons and Networks.
Fool's Gold: Extracting Finite State Machines from Recurrent Network Dynamics.
Asynchronous Dynamics of Continuous Time Neural Networks.
Optimal Signalling in Attractor Neural Networks.
Optimal Stochastic Search and Adaptive Momentum.
Correlation Functions in a Large Stochastic Network.
How to Describe Neuronal Activity: Spikes, Rates, or Assemblies?
Observability of Neural Network Behavior.
Coupled Dynamics of Fast Neurons and Slow Interactions.
The Statistical Mechanics of k-Satisfaction.
On the Non-Existence of a Universal Learning Algorithm for Recurrent Neural Networks.
Solvable Models of Artificial Neural Networks.
Structured Machine Learning for Soft Classification with Smoothing Spline ANOVA and Stacked Tuning, Testing, and Evaluation.
Non-Linear Statistical Analysis and Self-Organizing Hebbian Networks.
Discontinuous Generalization in Large Committee Machines.
Cross-Validation Estimates ISME.
Backpropagation Convergence via Deterministic Nonmonotone Perturbed Minimization.
Counting Function Theorem for Multi-Layer Networks.
Generalization Error and the Expected Network Complexity.
Bounds on the Complexity of Recurrent Neural Network Implementations of Finite State Machines.
Optimality Criteria for LMS and Backpropagation.
Use of Bad Training Data for Better Predictions.
Recovering a Feed-Forward Net From Its Output.
Learning Curves: Asymptotic Values and Rate of Convergence.
How to Choose an Activation Function.
Agnostic PAC-Learning of Functions on Analog Neural Nets.
Optimal Stopping and Effective Machine Complexity in Learning.
An Optimization Method of Layered Neural Networks based on the Modified Information Criterion.
Learning in Compositional Hierarchies: Inducing the Structure of Objects from Data.
Constructive Learning Using Internal Representation Conflicts.
Generation of Internal Representation by alpha.
Optimal Brain Surgeon: Extensions and performance comparison.
Supervised Learning with Growing Cell Structures.
Adaptive knot Placement for Nonparametric Regression.
A Comparative Study of a Modified Bumptree Neural Network with Radial Basis Function Networks and the Standard Multi Layer Perceptron.
Backpropagation without Multiplication.
Combined Neural Networks for Time Series Analysis.
A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction.
Bayesian Backprop in Action: Pruning, Committees, Error Bars and an Application to Spectroscopy.
Bayesian Backpropagation Over I-O Functions Rather Than Weights.
Robust Parameter Estimation and Model Selection for Neural Network Regression.
Locally Adaptive Nearest Neighbor Algorithms.
The Power of Amnesia.
Efficient Computation of Complex Distance Metrics Using Hierarchical Filtering.
Assessing the Quality of Learned Local Models.
Fast Non-Linear Dimension Reduction.
Two Iterative Algorithms for Computing the Singular Value Decomposition from Input/Output Samples.
Unsupervised Parallel Feature Extraction from First Principles.
Training Neural Networks with Deficient Data.
Supervised learning from incomplete data via an EM approach.
Learning Classification with Unlabeled Data.
Central and Pairwise Data Clustering by Competitive Neural Networks.
Clustering with a Domain-Specific Distance Measure.
Structural and Behavioral Evolution of Recurrent Networks.
A Local Algorithm to Learn Trajectories with Stochastic Neural Networks.
Credit Assignment through Time: Alternatives to Backpropagation.
Grammatical Inference by Attentional Control of Synchronization in an Oscillating Elman Network.
Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation.
When will a Genetic Algorithm Outperform Hill Climbing.
Surface Learning with Applications to Lipreading.
Fast Pruning Using Principal Components.
Unsupervised Learning of Mixtures of Multiple Causes in Binary Data.
A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction.
Developing Population Codes by Minimizing Description Length.
Autoencoders, Minimum Description Length and Helmholtz Free Energy.