{"title": "Functional Models of Selective Attention and Context Dependency", "book": "Advances in Neural Information Processing Systems", "page_first": 1180, "page_last": 1181, "abstract": null, "full_text": "Functional Models of Selective Attention \n\nand Context Dependency \n\nThomas H. Hildebrandt \n\nDepartment of Electrical Engineering and Computer Science \nRoom 304 Packard Laboratory \n19 Memorial Drive West \nLehigh University \nBethlehem PA 18015-3084 \n\nthildebr@aragorn.eecs.lehigh.edu \n\nScope \n\nThis workshop reviewed and classified the various models which have emerged from \nthe general concept of selective attention and context dependency, and sought to \nidentify their commonalities. It was concluded that the motivation and mecha(cid:173)\nnism of these functional models are \"efficiency\" and ''factoring'', respectively. The \nworkshop focused on computational models of selective attention and context de(cid:173)\npendency within the realm of neural networks. We treated only ''functional'' mod(cid:173)\nels; computational models of biological neural systems, and symbolic or rule-based \nsystems were omitted from the discussion. \n\nPresentations \n\nThomas H. Hildebrandt presented the results of his recent survey of the lit(cid:173)\nerature on functional models of selective attention and context dependency. He \nset forth the notions that selective attention and context dependency are equiva(cid:173)\nlent, that the goal of these methods is to reduce computational requirements, and \nthat this goal is achieved by what amounts to factoring or a divide-and-conquer \ntechnique which takes advantage of nonlinearities in the problem. \nDaniel S. Levine (University of Texas at Arlington) showed how the gated dipole \nstructure often used in the ART models can be used to account for time-dependent \nphenomena such as habituation and overcompensation. His adjusted model appro(cid:173)\npriately modelled the public's adverse reaction to \"New Coke\". \n\nLev Goldfarb (University of New Brunswick) presented a formal model for in(cid:173)\nductive learning based on symbolic transformation systems and parametric distance \nfunctions as an alternative to the commonly used algebraic transformation system \nand Euclidean distance function. The drawbacks of the latter system were briefly \ndiscussed, and it was shown how this new formal system can give rise to learning \nmodels which overcome these problems. \n\n1180 \n\n\fFunctional Models of Selective Attention and Context Dependency \n\n1181 \n\nChalapathy Neti (IBM, Boca Raton) presented a model which he has used to \nincrease signal-to-noise ratio (SNR) in noisy speech signals. The model is based on \nadaptive filtering of frequency bands with a constant frequency to bandwidth ratio. \nThis thresholding in the wavelet domain gives results which are superior to similar \nmethods operating in the Adaptive Fourier domain. Several types of signal could \nbe detected with SNRs close to Odb. \nPaul N. Refenes (University of London Business School) demonstrated the need \nto take advantage of contextual information in attempting to model the capital \nmarkets. There exist some fundamental economic formulae, but they hold only in \nthe long term. The desire to model events on a finer time scale requires reference \nto significant factors within a smaller window. To do this effectively requires the \nidentification of appropriate short-term indicators, as mere overparameterization \nhas been shown to lead to negative results. \n\nJonathan A. Marshall (University of North Carolina) reviewed the EXIN model, \nwhich correctly encodes partially overlapping patterns as distinct activations in \nthe output layer, while allowing the simultaneous appearance of nonoverlapping \npatterns to give rise to multiple activations in the output layer. The model thus \nproduces a factored representation of complex scenes. \nAlbert Nigrin (American University) presented a model, similar in concept to the \nEXIN model. It correctly handles synonymous inputs by means of cross-inhibition \nof the links connecting the synonyms to the target node. \n\nThomas H. Hildebrandt also presented a model for adaptive classification based \non decision feedback equalization. The model shifts the decision boundaries of \nthe underlying classifier to compensate shifts in the statistics of the input. On \nhandwritten character classification, it outperformed an identical classifier which \nused only static decision boundaries. \n\nSummary \n\nAccording to Hildebrandt's first talk, the concepts underlying selective attention are \nquite broad and generally applicable. Large nonlinearities in the problem permit the \nuse of problem subdivision or factoring (by analogy with the factoring of a Boolean \nequation). Factoring is a good method for reducing the complexity of nonlinear \nsystems. \n\nThe talks by Levine and Refenes showed that context enters naturally into the de(cid:173)\nscription, formulation, and solution ofreal-world modelling problems. Those by Neti \nand Hildebrandt showed that specific reference to temporal context can result in \nimmediate performance gains. The presentations by Marshall and Nigrin provided \nmodels for appropriately encoding contexts involving overlapping and synonymous \npatterns, respectively. The talk by Goldfarb indicates that abandoning assumptions \nregarding linearity ab initio may lead to more powerful learning systems. Refer to \n[1] for further information. \n\nReferences \n\n[1] Hildebrandt, Thomas H. Neural Network Models for Selective Attention and \n\nContext Dependency. Submitted to Neural Networks, December 1993. \n\n\f", "award": [], "sourceid": 813, "authors": [{"given_name": "Thomas", "family_name": "Hildebrandt", "institution": null}]}