Advances in Neural Information Processing Systems 7: Proceedings of the 1994 Conference

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November 28-December 1, 1994, Denver, Colorado

NIPS is the longest running annual meeting devoted to Neural Information Processing Systems. Drawing on such disparate domains as neuroscience, cognitive science, computer science, statistics, mathematics, engineering, and theoretical physics, the papers collected in the proceedings of NIPS7 reflect the enduring scientific and practical merit of a broad-based, inclusive approach to neural information processing. The primary focus ...

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Overview

November 28-December 1, 1994, Denver, Colorado

NIPS is the longest running annual meeting devoted to Neural Information Processing Systems. Drawing on such disparate domains as neuroscience, cognitive science, computer science, statistics, mathematics, engineering, and theoretical physics, the papers collected in the proceedings of NIPS7 reflect the enduring scientific and practical merit of a broad-based, inclusive approach to neural information processing. The primary focus remains the study of a wide variety of learning algorithms and architectures, for both supervised and unsupervised learning. The 139 contributions are divided into eight parts: Cognitive Science, Neuroscience, Learning Theory, Algorithms and Architectures, Implementations, Speech and Signal Processing, Visual Processing, and Applications.

Topics of special interest include the analysis of recurrent nets, connections to HMMs and the EM procedure, and reinforcement- learning algorithms and the relation to dynamic programming. On the theoretical front, progress is reported in the theory of generalization, regularization, combining multiple models, and active learning. Neuroscientific studies range from the large-scale systems such as visual cortex to single-cell electrotonic structure, and work in cognitive scientific is closely tied to underlying neural constraints. There are also many novel applications such as tokamak plasma control, Glove-Talk, and hand tracking, and a variety of hardware implementations, with particular focus on analog VLSI.

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Editorial Reviews

Booknews
Contains the collected papers of the 1988 IEEE Conference on [title] held in Denver, CO. Major research topics are covered and application to areas such as signal processing, vision, speech, and motor control, simulation of neural networks, and advances in hardware technology. Collected papers from the 1989 IEEE Conference on Neural Information Processing Systems--Natural and Synthetic, held in Denver, CO, run the gamut of topics: neuroscience, speech and signal processing, vision, optimization and control, hardware implementation, and history of neural networks. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780262201049
  • Publisher: MIT Press
  • Publication date: 7/6/1995
  • Series: Bradford Books Series
  • Pages: 1167
  • Product dimensions: 7.00 (w) x 10.00 (h) x 2.70 (d)

Meet the Author

Todd K. Leen is Professor of Computer Science and Engineering, and of Electrical and Computer Engineering, at Oregon Graduate Institute of Science and Technology.
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Table of Contents

Preface
Contributors
Part I Cognitive Science
Direction Selectivity in Primary Visual Cortex Using Massive Intracortical Connections
On the Computational Utility of Consciousness
Catastrophic Interference in Human Motor Learning
Grammar Learning by a Self-Organizing Network
Patterns of Damage in Neural Networks: The Effects of Lesion Area, Shape and Number
Forward Dynamic Models in Human Motor Control Psychophysical Evidence
A Solvable Connectionist Model of Immediate Recall of Ordered Lists
Part II Neuroscience
A Model for Chemosensory Reception
The Electronic Transformation: A Tool for Relating Neuronal Form to Function
A Model of the Hippocampus Combining Self-Organization and Associative Memory Function
A Model of Biological Neuron as a Temporal Neural Network
A Critical Comparison of Models for Orientation and Ocular Dominance Columns in the Striate Cortex
A Novel Reinforcement Model of Birdsong Vocalization Learning
Ocular Dominance and Patterned Lateral Connections in a Self-Organizing Model of the Primary Visual Cortex
Anatomical Origin and Computational Role of Diversity in the Response Properties of Cortical Neurons
Reinforcement Learning Predicts the Site of Plasticity for Auditory Remapping in the Barn Owl
Morphogenesis of the Lateral Geniculate Nucleus: How Singularities Affect Global Structure
A Computational Model of Prefrontal Cortex Function
A Neural Model of Delusions and Hallucinations in Schizophrenia
Spatial Representations in the Parietal Cortex May Use Basis Functions
Grouping Components of Three-Dimensional Moving Objects in Area MST of Visual Cortex
A Model of the Neural Basis of the RAT'SSENSE OF DIRECTION
Part III Learning Theory and Dynamics
On the Computational Complexity of Networks of Spiking Neurons
H∞ Optimal Training Algorithms and Their Relation to Back Propagation
Synchrony and Desynchrony in Neural Oscillator Networks
Learning in Large Linear Perceptrons and Why the Thermodynamic Limit Is Relevant to the Real World
Generalisation in Feedforward Networks
From Data Distributions to Regularization in Invariant Learning
Neural Network Ensembles, Cross Validation, and Active Learning
Limits on Learning Machine Accuracy Imposed by Data Quality
Higher Order Statistical Decorrelation without Information Loss
Hyperparameters, Evidence and Generalisation for an Unrealisable Rule
Temporal Dynamics of Generalization in Neural Networks
Stochastic Dynamics of Three-State Neural Networks
Learning Stochastic Perceptrons under K-Blocking Distributions
Learning from Queries for Maximum Information Gain in Imperfectly Learnable Problems
Bias, Variance and the Combination of Least Squares Estimators
On-Line Learning of Dichotomies
Dynamic Modelling of Chaotic Time Series with Neural Networks
A Rigorous Analysis of Linsker-Type Hebbian Learning
Sample Size Requirements for Feedforward Neural Networks
Asymptotics of Gradient-Based Neural Network Training Algorithms
Part IV Reinforcement Learning
Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems
Advantage Updating Applied to a Differential Game
Reinforcement Learning with Soft State Aggregation
Generalization in Reinforcement Learning: Safely Approximating the Value Function
Instance-Based State Identification for Reinforcement Learning
Finding Structure in Reinforcement Learning
Reinforcement Learning Methods for Continuous-Time Markov Decision Problems
An Actor/Critic Algorithm that Is Equivalent to Q-Learning
Part V Algorithms and Architectures
Financial Applications of Learning from Hints (Invited Paper)
Combining Estimators Using Non-Constant Weighting Functions
An Input Output HMM Architecture
Boltzmann Chains and Hidden Markov Models
Bayesian Query Construction for Neural Network Models
Using a Saliency Map for Active Spatial Selective Attention: Implementation & Initial Results
Multidimensional Scaling and Data Clustering
A Non-Linear Information Maximisation Algorithm that Performs Blind Separation
Plasticity-Mediated Competitive Learning
Phase-Space Learning
Learning Local Error Bars for Nonlinear Regression
Dynamic Cell Structures
Extracting Rules from Artificial Neural Networks with Distributed Representations
Capacity and Information Efficiency of a Brain-Like Associative Net
Boosting the Performance of RBF Networks with Dynamic Decay Adjustment
Simplifying Neural Nets BY Discovering Flat Minima
Learning with Product Units
Deterministic Annealing Variant of the EM Algorithm
Diffusion of Credit in Markovian Models
Factorial Learning by Clustering Features
Interior Point Implementations of Alternating Minimization Training
SARDNET: A Self-Organizing Feature Map for Sequences
Convergence Properties of the K-Means Algorithms
Active Learning for Function Approximation
Analysis of Unstandardized Contributions in Cross Connected Networks
Template-Based Algorithms for Connectionist Rule Extraction
Factorial Learning and the EM Algorithm
A Growing Neural Gas Network Learns Topologies
An Alternative Model for Mixtures of Experts
Estimating Conditional Probability Densities for Periodic Variables
Effects of Noise on Convergence and Generalization in Recurrent Networks
Learning Many Related Tasks at the Same Time with Backpropagation
A Rapid Graph-Based Method for Arbitrary Transformation-Invariant Pattern Classification
Recurrent Networks: Second Order Properties and Pruning
Classifying with Gaussian Mixtures and Clusters
Efficient Methods for Dealing with Missing Data in Supervised Learning
An Experimental Comparison of Recurrent Neural Networks
Active Learning with Statistical Models
Learning with Preknowledge: Clustering with Point and Graph Matching Distance Measures
Direct Multi-Step Time Series Prediction Using TD(λ)
Part VI Implementations
ICEG Morphology Classification Using an Analogue VLSI Neural Network
A Silicon Axon
The NI1000: High Speed Parallel VLSI For Implementing Multilayer Perceptrons
A Real Time Clustering CMOS Neural Engine
Pulsestream Synapses with Non-Volatile Analogue Amorphous-Silicon Memories
A Lagrangian Formulation for Optical Backpropagation Training in Kerr-Type Optical Networks
A Charge-Based CMOS Parallel Analog Vector Quantizer
An Auditory Localization and Coordinate Transform Chip
An Analog Neural Network Inspired by Fractal Block Coding
A Study of Parallel Perturbative Gradient Descent
Implementation of Neural Hardware with the Neural VLSI of Uran in Applications with Reduced Representations
Single Transistor Learning Synapses
Part VII Speech and Signal Processing
Pattern Playback in the '90S (Invited Paper)
Non-Linear Prediction of Acoustic Vectors Using Hierarchical Mixtures of Experts
GLOVE-TALKII: Mapping Hand Gestures to Speech Using Neural Networks
Visual Speech Recognition with Stochastic Networks
Hierarchical Mixtures of Experts Methodology Applied to Continuous Speech Recognition
Connectionist Speaker Normalization with Generalized Resource Allocating Networks
Using Voice Transformations to Create Additional Training Talkers for Word Spotting
A Comparison of Discrete-Time Operator Models for Nonlinear System Identification
Part VIII Visual Processing
Learning Saccadic Eye Movements Using Multiscale Spatial Filters
A Convolutional Neural Network Hand Tracker
Correlation and Interpolation Networks for Real-Time Expression Analysis/Synthesis
Learning Direction in Global Motion: Two Classes of Psychophysically-Motivated Models
Associative Decorrelation Dynamics: A Theory of Self-Organization and Optimization in Feedback Networks
JPMAX: Learning to Recognize Moving Objects as a Model-Fitting Problem
PCA-Pyramids for Image Compression
Unsupervised Classification of 3D Objects from 2D Views
New Algorithms for 2D And 3D Point Matching: Pose Estimation and Correspondence
Using a Neural Net to Instantiate a Deformable Model
Nonlinear Image Interpolation Using Manifold Learning
Coarse-to-Fine Image Search Using Neural Networks
Part IX Applications
Transformation Invariant Autoassociation with Application to Handwritten Character Recognition
Learning Prototype Models for Tangent Distance
Real-Time Control of Tokamak Plasma Using Neural Networks
Recognizing Handwritten Digits Using Mixtures of Linear Models
Optimal Movement Primitives
An Integrated Architecture of Adaptive Neural Network Control for Dynamic Systems
A Connectionist Technique for Accelerated Textual Input: Letting a Network Do the Typing
Predictive Coding with Neural Nets: Application to Text Compression
Predicting the Risk of Complications in Coronary Artery Bypass Operations Using Neural Networks
Comparing the Prediction Accuracy of Artificial Neural Networks and Other Statistical Models for Breast Cancer Survival
Learning to Play the Game of Chess
A Mixture Model System for Medical and Machine Diagnosis
Inferring Ground Truth from Subjective Labelling of Venus Images
The Use of Dynamic Writing Information in a Connectionist On-Line Cursive Handwriting Recognition System
Adaptive Elastic Input Field for Recognition Improvement
Pairwise Neural Network Classifiers with Probabilistic Outputs
Interference in Learning Internal Models of Inverse Dynamics in Humans
Computational Structure of Coordinate Transformations: A Generalization Study
Author Index
Keyword Index
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