Above are papers related to the following paper: [Paper_ID] Paper #739816. 	[Title] Learning to segment images using dynamic feature binding an isolated object in an image is [Abstract] Despite the fact that complex visual scenes contain multiple, overlapping objects, people perform object recognition with ease and accuracy. One operation that facilitates recognition is an early segmentation process in which features of objects are grouped and labeled according to which object they belong. Current computational systems that perform this operation are based on predefined grouping heuristics. We describe a system called MAGIC that learns how to group features based on a set of presegmented examples. In many cases, MAGIC discovers grouping heuristics similar to those previously proposed, but it also has the capability of finding nonintuitive structural regularities in images. Grouping is performed by a relaxation network that attempts to dynamically bind related features. Features transmit a complex-valued signal (amplitude and phase) to one another; binding can thus be represented by phase locking related features. MAGIC's training procedure is a generalization of recurrent back propagation to complex-valued units. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #523010. 	[Title] The end of the line for a brain-damaged model of unilateral neglect [Abstract] For over a century, it has been known that damage to the right hemisphere of the brain can cause patients to be unaware of the contralesional side of space. This condition, known as unilateral neglect, represents a collection of clinically related spatial disorders characterized by the failure in free vision to respond, explore, or orient to stimuli predominantly located on the side of space opposite the damaged hemisphere. Recent studies using the simple task of line bisection, a conventional diagnostic test, have proved surprisingly revealing with respect to the spatial and attentional impairments involved in neglect. In line bisection, the patient is asked to mark the midpoint of a thin horizontal line on a sheet of paper. Neglect patients generally transect far to the right of the center. Extensive studies of line bisection have been conducted, manipulating|among other factors|line length, orientation, and position. We have simulated the pattern of results using an existing computational model of visual perception and selective attention called morsel (Mozer, 1991). morsel has already been used to model data in a related disorder, neglect dyslexia (Mozer & Behrmann, 1990). In this earlier work, morsel was "lesioned" in accordance with the damage we suppose to have occurred in the brains of Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1140548. 	[Title] Lending Direction to Neural Networks [Abstract] We present a general formulation for a network of stochastic directional units. This formulation is an extension of the Boltzmann machine in which the units are not binary, but take on values on a cyclic range, between 0 and 2 radians. This measure is appropriate to many domains, representing cyclic or angular values, e.g., wind direction, days of the week, phases of the moon. The state of each unit in a Directional-Unit Boltzmann Machine (DUBM) is described by a complex variable, where the phase component specifies a direction; the weights are also complex variables. We associate a quadratic energy function, and corresponding probability, with each DUBM configuration. The conditional distribution of a unit's stochastic state is a circular version of the Gaussian probability distribution, known as the von Mises distribution. In a mean-field approximation to a stochastic dubm, the phase component of a unit's state represents its mean direction, and the magnitude component specifies the degree of certainty associated with this direction. This combination of a value and a certainty provides additional representational power in a unit. We present a proof that the settling dynamics for a mean-field DUBM cause convergence to a free energy minimum. Finally, we describe a learning algorithm and simulations that demonstrate a mean-field DUBM's ability to learn interesting mappings. fl To appear in: Neural Networks. 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 #197452. 	[Title] Best-First Model Merging for Dynamic Learning and Recognition [Abstract] Best-first model merging is a general technique for dynamically choosing the structure of a neural or related architecture while avoiding overfitting. It is applicable to both learning and recognition tasks and often generalizes significantly better than fixed structures. We demonstrate the approach applied to the tasks of choosing radial basis functions for function learning, choosing local affine models for curve and constraint surface modelling, and choosing the structure of a balltree or bumptree to maximize efficiency of access. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #120817. 	[Title] Control of Selective Visual Attention: Modeling the "Where" Pathway [Abstract] Intermediate and higher vision processes require selection of a subset of the available sensory information before further processing. Usually, this selection is implemented in the form of a spatially circumscribed region of the visual field, the so-called "focus of attention" which scans the visual scene dependent on the input and on the attentional state of the subject. We here present a model for the control of the focus of attention in primates, based on a saliency map. This mechanism is not only expected to model the functionality of biological vision but also to be essential for the understanding of complex scenes in machine vision. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #43186. 	[Title] Learning Feature-based Semantics with Simple Recurrent Networks [Abstract] The paper investigates the possibilities for using simple recurrent networks as transducers which map sequential natural language input into non-sequential feature-based semantics. The networks perform well on sentences containing a single main predicate (encoded by transitive verbs or prepositions) applied to multiple-feature objects (encoded as noun-phrases with adjectival modifiers), and shows robustness against ungrammatical inputs. A second set of experiments deals with sentences containing embedded structures. Here the network is able to process multiple levels of sentence-final embeddings but only one level of center-embedding. This turns out to be a consequence of the network's inability to retain information that is not reflected in the outputs over intermediate phases of processing. Two extensions to Elman's [9] original recurrent network architecture are introduced. 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 #1135894. 	[Title] L 0 |The First Four Years Abstract A summary of the progress and plans of [Abstract]  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 #739816 has the title `Learning to segment images using dynamic feature binding an isolated object in an image is
` and the abstract `Despite the fact that complex visual scenes contain multiple, overlapping objects, people perform object recognition with ease and accuracy. One operation that facilitates recognition is an early segmentation process in which features of objects are grouped and labeled according to which object they belong. Current computational systems that perform this operation are based on predefined grouping heuristics. We describe a system called MAGIC that learns how to group features based on a set of presegmented examples. In many cases, MAGIC discovers grouping heuristics similar to those previously proposed, but it also has the capability of finding nonintuitive structural regularities in images. Grouping is performed by a relaxation network that attempts to dynamically bind related features. Features transmit a complex-valued signal (amplitude and phase) to one another; binding can thus be represented by phase locking related features. MAGIC's training procedure is a generalization of recurrent back propagation to complex-valued units. 
`.
Question: Which category should Paper #739816 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #739816 has the title `Learning to segment images using dynamic feature binding an isolated object in an image is
` and the abstract `Despite the fact that complex visual scenes contain multiple, overlapping objects, people perform object recognition with ease and accuracy. One operation that facilitates recognition is an early segmentation process in which features of objects are grouped and labeled according to which object they belong. Current computational systems that perform this operation are based on predefined grouping heuristics. We describe a system called MAGIC that learns how to group features based on a set of presegmented examples. In many cases, MAGIC discovers grouping heuristics similar to those previously proposed, but it also has the capability of finding nonintuitive structural regularities in images. Grouping is performed by a relaxation network that attempts to dynamically bind related features. Features transmit a complex-valued signal (amplitude and phase) to one another; binding can thus be represented by phase locking related features. MAGIC's training procedure is a generalization of recurrent back propagation to complex-valued units. 
`.
Question: Which category should Paper #739816 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