Advances in Neural Information Processing Systems 8: Proceedings of the 1995 ConferenceDavid S. Touretzky, Michael C. Mozer, Michael E. Hasselmo MIT Press, 1996 - 1098 lappuses The past decade has seen greatly increased interaction between theoretical work in neuroscience, cognitive science and information processing, and experimental work requiring sophisticated computational modeling. The 152 contributions in NIPS 8 focus on a wide variety of algorithms and architectures for both supervised and unsupervised learning. They are divided into nine parts: Cognitive Science, Neuroscience, Theory, Algorithms and Architectures, Implementations, Speech and Signal Processing, Vision, Applications, and Control. Chapters describe how neuroscientists and cognitive scientists use computational models of neural systems to test hypotheses and generate predictions to guide their work. This work includes models of how networks in the owl brainstem could be trained for complex localization function, how cellular activity may underlie rat navigation, how cholinergic modulation may regulate cortical reorganization, and how damage to parietal cortex may result in neglect. Additional work concerns development of theoretical techniques important for understanding the dynamics of neural systems, including formation of cortical maps, analysis of recurrent networks, and analysis of self- supervised learning. Chapters also describe how engineers and computer scientists have approached problems of pattern recognition or speech recognition using computational architectures inspired by the interaction of populations of neurons within the brain. Examples are new neural network models that have been applied to classical problems, including handwritten character recognition and object recognition, and exciting new work that focuses on building electronic hardware modeled after neural systems. A Bradford Book |
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1.5. rezultāts no 74.
... Approximation and Learning of Trajectories Using Oscillators P. BALDI , K. HORNIK 451 A Smoothing Regularizer for Recurrent Neural Networks 458 L. WU , J. MOODY EM Optimization of Latent - Variable Density Models C. M. BISHOP , M ...
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Saturs
Learning the Structure of Similarity | 3 |
Human Reading and the Curse of Dimensionality | 17 |
Harmony Networks Do Not Work | 31 |
Rapid Quality Estimation of Neural Network Input Representations | 45 |
Modeling Interactions of the Rats Place and Head Direction Systems | 61 |
Information through a Spiking Neuron | 75 |
A Dynamical Model of Context Dependencies for the VestibuloOcular Reflex | 89 |
When Is an Integrateandfire Neuron like a Poisson Neuron? | 103 |
Not All Weights Are Created Equal | 563 |
Investment Learning with Hierarchical PSOMS | 570 |
T LIN B G HORNE P TIÑO C L GILES | 584 |
A Practical Monte Carlo Implementation of Bayesian Learning | 598 |
Finite State Automata that Recurrent CascadeCorrelation Cannot Represent | 612 |
Benchmarks in Combinatorial Optimization | 626 |
Is Learning the nth Thing Any Easier Than Learning the First? | 640 |
Learning Sparse Perceptrons | 654 |
The Geometry of Eye Rotations and Listings Law | 117 |
Cholinergic Suppression of Transmission May Allow Combined Associative | 131 |
Independent Component Analysis of Electroencephalographic Data | 145 |
Plasticity of CenterSurround Opponent Receptive Fields in Real and Artificial | 159 |
Statistical Theory of OvertrainingIs CrossValidation Asymptotically | 176 |
How Overfitting Can Be Useful | 190 |
Neural Networks with Quadratic VC Dimension | 197 |
On the Computational Power of Noisy Spiking Neurons | 211 |
Stable Dynamic Parameter Adaptation | 225 |
Recursive Estimation of Dynamic Modular RBF Networks | 239 |
Modern Analytic Techniques to Solve the Dynamics of Recurrent Neural | 253 |
Generalisation of a Class of Continuous Neural Networks | 267 |
Optimization Principles for the Neural Code | 281 |
Active Learning in Multilayer Perceptrons | 295 |
Worstcase Loss Bounds for Single Neurons | 309 |
Adaptive BackPropagation in Online Learning of Multilayer Networks | 323 |
Quadratictype Lyapunov Functions for Competitive Neural Networks with | 337 |
Bayesian Methods for Mixtures of Experts | 351 |
Geometry of Early Stopping in Linear Networks | 365 |
Adaptive Mixture of Probabilistic Transducers | 381 |
Recurrent Neural Networks for Missing or Asynchronous Data | 395 |
Discriminant Adaptive Nearest Neighbor Classification and Regression | 409 |
Generalized Learning Vector Quantization | 423 |
Symplectic Nonlinear Component Analysis | 437 |
Universal Approximation and Learning of Trajectories Using Oscillators | 451 |
EM Optimization of LatentVariable Density Models | 465 |
Boosting Decision Trees | 479 |
Hierarchical Recurrent Neural Networks for Longterm Dependencies | 493 |
Using Pairs of Data Points to Define Splits for Decision Trees | 507 |
YOBD YOBS | 523 |
W OPITZ J W SHAVLIK | 536 |
Explorations with the Dynamic Wave Model | 549 |
Improved Silicon Cochlea Using Compatible Lateral Bipolar Transistors | 671 |
NeuronMOS Temporal Winner Search Hardware for Fullyparallel Data | 685 |
Silicon Models for Auditory Scene Analysis | 699 |
Model Matching and SFMD Computation | 713 |
Onsetbased Sound Segmentation | 729 |
Forwardbackward Retraining of Recurrent Neural Networks | 743 |
A New Learning Algorithm for Blind Signal Separation | 757 |
B LEMARIÉ M GILLOUX M LEROUX | 771 |
The Gamma MLP for Speech Phoneme Recognition | 785 |
A Framework for Nonrigid Matching and Correspondence | 795 |
Unsupervised Pixelprediction | 809 |
Classifying Facial Action | 823 |
A Model of Transparent Motion and Nontransparent Motion Aftereffects | 837 |
Empirical Entropy Manipulation for Realworld Problems | 851 |
A Viewbased Approach to 3D Object Recognition Using Multiple | 865 |
Improving Committee Diagnosis with Resampling Techniques | 882 |
Memorybased Learning of | 896 |
Visual Gesturebased Robot Guidance with a Modular Neural System | 903 |
Prediction of Beta Sheets in Proteins | 917 |
Using Feedforward Neural Networks to Monitor Alertness from Changes in | 931 |
A Memorybased Reinforcement Learning Approach | 945 |
Rankprop and Multitask Learning | 959 |
Experiments with Neural Networks for Real Time Implementation of Control | 973 |
A Dynamical Systems Approach for a Learnable Autonomous Robot | 989 |
Learning Fine Motion by Markov Mixtures of Experts | 1003 |
Improving Elevator Performance Using Reinforcement Learning | 1017 |
Competence Acquisition in an Autonomous Mobile Robot Using Hardware | 1031 |
Stable Linear Approximations to Dynamic Programming for Stochastic Control | 1045 |
Improving Policies without Measuring Merits | 1059 |
Temporal Difference in Learning in Continuous Time and Space | 1073 |
1087 | |