摘要:

Contributions, the products of hidden unit activations and weights, are presented as a valuable tool for investigating the inner workings of neural nets. Using a scaled-down version of NETtalk, a fully automated method for summarizing in a compact form both local and distributed hidden-unit responsibilities is demonstrated. Contributions are shown to be more useful for ascertaining hidden-unit responsibilities than either weights or hidden-unit activations. Among the results yielded by contribution analysis: for the example net, redundant output units are handled by identical patterns of hidden units, and the amount of responsibility a hidden unit takes on is inversely proportional to the number of hidden units.

ISSN:0954-0091    

DOI:10.1080/09540098908915632

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

摘要:

This paper specifies the main features of connectionist and brain-like connectionist models; argues for the need for, and usefulness of, appropriate successively larger brainlike structures; and examines parallel-hierarchical Recognition Cone models of perception from this perspective, as examples of networks exploiting such structures (e.g. local receptive fields, global convergence-divergence). The anatomy, physiology, behavior, and development of the visual system are briefly summarized to motivate the architecture of brain-structured networks for perceptual recognition. Results are presented from simulations of carefully pre-designed Recognition Cone structures that perceive objects (e.g. houses) in digitized photographs. A framework for perceptual learning is introduced, including mechanisms for generation learning, i.e. the growth of new links and possibly, nodes, subject to brain-like topological constraints. The information processing transforms discovered through feedback-guided generation are fine-tuned by feedback-guided reweighting of links. Some preliminary results are presented of brain-structured networks that learn to recognize simple objects (e.g. letters of the alphabet, cups, apples, bananas) through generation and reweighting of transforms. These show large improvements over networks that either lack brain-like structure or/and learn by reweighting of links alone. It is concluded that brain-like structures and generation learning can significantly increase the power of connectionist models.

ISSN:0954-0091    

DOI:10.1080/09540098908915633

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

摘要:

Essentially all work in connectionist learning up to now has been induction from examples, but instruction is as important in symbolic artificial intelligence as it is in nature. This paper describes an implemented connectionist learning system that transforms an instruction expressed in a description language into an input for a connectionist knowledge representation system, which in turn changes the network in order to integrate new knowledge. Integration is always important when a single new fact causes changes in several parts of the knowledge-base; it is an adjustment which cannot easily be done with learning-by-example techniques only. The new, integrated knowledge can be used in conjunction with prior knowledge. The learning method used is recruitment learning, a technique which converts network units from a pool of free units into units which carry meaningful information, i.e represent generic concepts.

ISSN:0954-0091    

DOI:10.1080/09540098908915634

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

摘要:

Connectionist models have had problems representing and applying general knowledge rules that specifically require variables. This variable binding problem has barred them from performing the high-level inferencing necessary for planning, reasoning, and natural language understanding. This paper describes ROBIN, a structured neural network model capable of high-level inferencing requiring variable bindings and rule application. Variable bindings are handled by signatures—activation patterns which uniquely identify the concept bound to a role. Signatures allow multiple role-bindings to be propagated across the network in parallel for rule application and dynamic inference path instantiation. Signatures are integrated within a connectionist semantic network structure whose constraint-relaxation process selects between those newly-instantiated inferences. This allows ROBIN to handle an area of high-level inferencing difficult even for symbolic models, that of resolving multiple constraints from context to select the best interpretation from among several alternative and possibly ambiguous inference paths.

ISSN:0954-0091    

DOI:10.1080/09540098908915635

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

ISSN:0954-0091    

DOI:10.1080/09540098908915636

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

ISSN:0954-0091    

DOI:10.1080/09540098908915637

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

ISSN:0954-0091    

DOI:10.1080/09540098908915638

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor