Above are papers related to the following paper: [Paper_ID] Paper #1135589. 	[Title] Book Review New Kids on the Block way in the field of connectionist modeling. The [Abstract] Connectionist Models is a collection of forty papers representing a wide variety of research topics in connectionism. The book is distinguished by a single feature: the papers are almost exclusively contributions of graduate students active in the field. The students were selected by a rigorous review process and participated in a two week long summer school devoted to connectionism 2 . As the ambitious editors state in the foreword: These are bold claims and, if true, the reader is presented with an exciting opportunity to sample the frontiers of connectionism. Their words imply two ways to approach the book. The book must be read not just as a random collection of scientific papers, but also as a challenge to evaluate a controversial field. 2 This summer school is actually the third in a series, previous ones being held in 1986 and 1988. The proceedings of the 1988 summer school (which I had the priviledge of participating in) are reviewed by Nigel Goddard in [4]. Continuing the pattern, a fourth school is scheduled to be held in 1993 in Boulder, CO. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8581. 	[Title] LEARNING FACTORIAL CODES BY PREDICTABILITY MINIMIZATION (Neural Computation, 4(6):863-879, 1992) [Abstract] I propose a novel general principle for unsupervised learning of distributed non-redundant internal representations of input patterns. The principle is based on two opposing forces. For each representational unit there is an adaptive predictor which tries to predict the unit from the remaining units. In turn, each unit tries to react to the environment such that it minimizes its predictability. This encourages each unit to filter `abstract concepts' out of the environmental input such that these concepts are statistically independent of those upon which the other units focus. I discuss various simple yet potentially powerful implementations of the principle which aim at finding binary factorial codes (Bar-low et al., 1989), i.e. codes where the probability of the occurrence of a particular input is simply the product of the probabilities of the corresponding code symbols. Such codes are potentially relevant for (1) segmentation tasks, (2) speeding up supervised learning, (3) novelty detection. Methods for finding factorial codes automatically implement Occam's razor for finding codes using a minimal number of units. Unlike previous methods the novel principle has a potential for removing not only linear but also non-linear output redundancy. Illustrative experiments show that algorithms based on the principle of predictability minimization are practically feasible. The final part of this paper describes an entirely local algorithm that has a potential for learning unique representations of extended input sequences. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #135464. 	[Title] Unsupervised Neural Network Learning Procedures For Feature Extraction and Classification [Abstract] Technical report CNS-TR-95-1 Center for Neural Systems McMaster University Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #255628. 	[Title] Efficient Visual Search: A Connectionist Solution [Abstract] Searching for objects in scenes is a natural task for people and has been extensively studied by psychologists. In this paper we examine this task from a connectionist perspective. Computational complexity arguments suggest that parallel feed-forward networks cannot perform this task efficiently. One difficulty is that, in order to distinguish the target from distractors, a combination of features must be associated with a single object. Often called the binding problem, this requirement presents a serious hurdle for connectionist models of visual processing when multiple objects are present. Psychophysical experiments suggest that people use covert visual attention to get around this problem. In this paper we describe a psychologically plausible system which uses a focus of attention mechanism to locate target objects. A strategy that combines top-down and bottom-up information is used to minimize search time. The behavior of the resulting system matches the reaction time behavior of people in several interesting tasks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1133004. 	[Title] : Running Title: Local Multivariate Binary Processors [Abstract] We thank Sue Becker, Peter Hancock and Darragh Smyth for helpful comments on this work. The work of Dario Floreano and Bill Phillips was supported by a Network Grant from the Human Capital and Mobility Programme of the European Community. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #237489. 	[Title] A Non-linear Information Maximisation Algorithm that Performs Blind Separation. [Abstract] A new learning algorithm is derived which performs online stochastic gradient ascent in the mutual information between outputs and inputs of a network. In the absence of a priori knowledge about the `signal' and `noise' components of the input, propagation of information depends on calibrating network non-linearities to the detailed higher-order moments of the input density functions. By incidentally minimising mutual information between outputs, as well as maximising their individual entropies, the network `fac-torises' the input into independent components. As an example application, we have achieved near-perfect separation of ten digitally mixed speech signals. Our simulations lead us to believe that our network performs better at blind separation than the Herault-Jutten network, reflecting the fact that it is derived rigorously from the mutual information objective. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #120084. 	[Title] VISIT: An Efficient Computational Model of Human Visual Attention [Abstract] One of the challenges for models of cognitive phenomena is the development of efficient and exible interfaces between low level sensory information and high level processes. For visual processing, researchers have long argued that an attentional mechanism is required to perform many of the tasks required by high level vision. This thesis presents VISIT, a connectionist model of covert visual attention that has been used as a vehicle for studying this interface. The model is efficient, exible, and is biologically plausible. The complexity of the network is linear in the number of pixels. Effective parallel strategies are used to minimize the number of iterations required. The resulting system is able to efficiently solve two tasks that are particularly difficult for standard bottom-up models of vision: computing spatial relations and visual search. Simulations show that the networks behavior matches much of the known psychophysical data on human visual attention. The general architecture of the model also closely matches the known physiological data on the human attention system. Various extensions to VISIT are discussed, including methods for learning the component modules. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1140543. 	[Title] Computational modeling of spatial attention [Abstract]  Please summarise all the given information.

Paper #1135589 has the title `Book Review New Kids on the Block way in the field of connectionist modeling. The
` and the abstract `Connectionist Models is a collection of forty papers representing a wide variety of research topics in connectionism. The book is distinguished by a single feature: the papers are almost exclusively contributions of graduate students active in the field. The students were selected by a rigorous review process and participated in a two week long summer school devoted to connectionism 2 . As the ambitious editors state in the foreword: These are bold claims and, if true, the reader is presented with an exciting opportunity to sample the frontiers of connectionism. Their words imply two ways to approach the book. The book must be read not just as a random collection of scientific papers, but also as a challenge to evaluate a controversial field. 2 This summer school is actually the third in a series, previous ones being held in 1986 and 1988. The proceedings of the 1988 summer school (which I had the priviledge of participating in) are reviewed by Nigel Goddard in [4]. Continuing the pattern, a fourth school is scheduled to be held in 1993 in Boulder, CO. 
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Question: Which category should Paper #1135589 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1135589 has the title `Book Review New Kids on the Block way in the field of connectionist modeling. The
` and the abstract `Connectionist Models is a collection of forty papers representing a wide variety of research topics in connectionism. The book is distinguished by a single feature: the papers are almost exclusively contributions of graduate students active in the field. The students were selected by a rigorous review process and participated in a two week long summer school devoted to connectionism 2 . As the ambitious editors state in the foreword: These are bold claims and, if true, the reader is presented with an exciting opportunity to sample the frontiers of connectionism. Their words imply two ways to approach the book. The book must be read not just as a random collection of scientific papers, but also as a challenge to evaluate a controversial field. 2 This summer school is actually the third in a series, previous ones being held in 1986 and 1988. The proceedings of the 1988 summer school (which I had the priviledge of participating in) are reviewed by Nigel Goddard in [4]. Continuing the pattern, a fourth school is scheduled to be held in 1993 in Boulder, CO. 
`.
Question: Which category should Paper #1135589 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks