Otherwise it might ascribe different characteristics to the same object under different lights. But the first step in using color for recognition, segmentingthe scene into regions of different colors, does not require color constancy.

The network uses the pattern of activation over a set of hidden units from time-step tl, together with element t, to predict element t 1. When the network is trained with strings from a particular finite-state grammar, it can learn to be a perfect finite-state recognizer for the grammar. Cluster analyses of the hidden-layer patterns of activation showed that they encode prediction-relevant information about the entire path traversed through the network. We illustrate the phases of learning with cluster analyses performed at different points during training. Several connectionist architectures that are explicitly constrained to capture sequential infonnation have been developed. Examples are Time Delay Networks (e.g.

We introduce an optimization approach for solving problems in computer visionthat involve multiple levels of abstraction. Our objective functions include compositional and specialization hierarchies. We cast vision problems as inexact graph matching problems, formulate graph matching in terms of constrained optimization, and use analog neural networks to perform the optimization. The method is applicable to perceptual groupingand model matching. Preliminary experimental results are shown.

We discuss synthetic receptors for haptic sensing. These are based on magnetic field sensors (Hall effect structures) fabricated using standard CMOS technologies.

A new optimization strategy, Mean Field Annealing, is presented. Its application to MAP restoration of noisy range images is derived and experimentally verified.

We address the question of when a network can be expected to generalize from m random training examples chosen from some arbitrary probabilitydistribution, assuming that future test examples are drawn from the same distribution. Among our results are the following bounds on appropriate sample vs. network size.

David Rogers Research Institute for Advanced Computer Science MS 230-5, NASA Ames Research Center Moffett Field, CA 94035 ABSTRACT A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is presented. In conditions of near-or over-capacity, where the associative-memory behavior of the model breaksdown, the processing performed by the model can be interpreted asthat of a statistical predictor. Mathematical results are presented which serve as the framework for a new statistical viewpoint ofsparse distributed memory and for which the standard formulation ofSDM is a special case. This viewpoint suggests possible enhancements to the SDM model, including a procedure for improving the predictiveness of the system based on Holland's work with'Genetic Algorithms', and a method for improving the capacity of SDM even when used as an associative memory. OVERVIEW This work is the result of studies involving two seemingly separate topics that proved to share a common framework.

The issues of scaling and generalization have emerged as key issues in current studies of supervised learning from examples in neural networks. Questions such as how many training patterns and training cycles are needed for a problem of a given size and difficulty, how to represent the inllUh and how to choose useful training exemplars, are of considerable theoretical and practical importance. Several intuitive rules of thumb have been obtained from empirical studies, but as yet there are few rigorous results.In this paper we summarize a study Qf generalization in the simplest possible case-perceptron networks learning linearly separable functions.The task chosen was the majority function (i.e. return a 1 if a majority of the input units are on), a predicate with a number ofuseful properties. We find that many aspects of.generalization in multilayer networks learning large, difficult tasks are reproduced in this simple domain, in which concrete numerical results and even some analytic understanding can be achieved.

ABSTRACT This report describes the design of a programmable general purpose analog neural computer and simulator. It is intended primarily for real-world real-time computations such as analysis of visual or acoustical patterns, robotics and the development of special purpose neural nets. The machine is scalable and composed of interconnected modules containing arrays ofneurons, modifiable synapses and switches. It runs entirely in analog mode but connection architecture, synaptic gains and time constants as well as neuron parameters are set digitally. Each neuron has a limited number of inputs and can be connected to any but not all other neurons.

ALVINN (Autonomous Land Vehicle In a Neural Network) is a 3-layer back-propagation network designed for the task of road following. Currently ALVINNtakes images from a camera and a laser range finder as input and produces as output the direction the vehicle should travel in order to follow the road. Training has been conducted using simulated road images. Successful tests on the Carnegie Mellon autonomous navigation test vehicle indicate that the network can effectively follow real roads under certain field conditions. The representation developed to perfOIm the task differs dramatically whenthe networlc is trained under various conditions, suggesting the possibility of a novel adaptive autonomous navigation system capable of tailoring its processing to the conditions at hand.