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Objectbased attention and occlusion: Evidence from normal participants and a computational model
 Journal of Experimental Psychology: Human Perception and Performance
, 1998
"... One way of perceptually organizing a complex visual scene is to attend selectively to information in a particular physical location. Another way of reducing the complexity in the input is to attend selectively to an individual object in the scene and to process its elements preferentially. This latt ..."
Abstract

Cited by 48 (12 self)
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One way of perceptually organizing a complex visual scene is to attend selectively to information in a particular physical location. Another way of reducing the complexity in the input is to attend selectively to an individual object in the scene and to process its elements preferentially. This latter, objectbased attention process was examined, and the predicted superiority for reporting features from 1 relative to 2 objects was replicated in a series of experiments. This objectbased process was robust even under conditions of occlusion, although there were some boundary conditions on its operation. Finally, an account of the data is provided via simulations of the findings in a computational model. The claim is that objectbased attention arises from a mechanism that groups together those features based on internal representations developed over perceptual experience and then preferentially gates these features for later, selective processing. Humans are exceptionally good at recognizing objects in natural visual scenes despite the fact that such scenes usually contain multiple, overlapping objects. One way in which individuals organize this complex input to minimize the
The von Mises Graphical Model: Structure Learning
, 2011
"... The von Mises distribution is a continuous probability distribution on the circle used in directional statistics. In this paper, we introduce the undirected von Mises Graphical model and present an algorithm for structure learning using L1 regularization. We show that the learning algorithm is both ..."
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Cited by 2 (1 self)
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The von Mises distribution is a continuous probability distribution on the circle used in directional statistics. In this paper, we introduce the undirected von Mises Graphical model and present an algorithm for structure learning using L1 regularization. We show that the learning algorithm is both consistent and efficient. We also introduce a simple inference algorithm based on Gibbs sampling. We compare and contrast the von Mises Graphical Model (VGM) with a Gaussian Graphical Model (GGM) on both synthetic data and on data from protein structures and demonstrate that the The von Mises distribution is used in directional statistics to model angles and other circularlydistributed variables [3]. It closely approximates the wrapped normal distribution, but has the advantage that it is more tractable, mathematically [9]. Additionally, the von Mises distribution can be generalized to distributions over the (p − 1)dimensional sphere in R p, where it is known
The von Mises Graphical Model: Regularized Structure and Parameter Learning
, 2011
"... The von Mises distribution is a continuous probability distribution on the circle used in directional statistics. In this paper, we introduce the undirected von Mises Graphical model and present an algorithm for parameter and structure learning using L1 regularization. We show that the learning algo ..."
Abstract

Cited by 1 (1 self)
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The von Mises distribution is a continuous probability distribution on the circle used in directional statistics. In this paper, we introduce the undirected von Mises Graphical model and present an algorithm for parameter and structure learning using L1 regularization. We show that the learning algorithm is both consistent and statistically efficient. Additionally, we introduce a simple inference algorithm based on Gibbs sampling. We compare the von Mises Graphical Model (VGM) with a Gaussian Graphical Model (GGM) on both synthetic data and on data from protein structures, and demonstrate that the VGM achieves higher accuracy than the GGM.
Transformation Equivariant Boltzmann Machines
"... Abstract. We develop a novel modeling framework for Boltzmann machines, augmenting each hidden unit with a latent transformation assignment variable which describes the selection of the transformed view of the canonical connection weights associated with the unit. This enables the inferences of the ..."
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Cited by 1 (0 self)
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Abstract. We develop a novel modeling framework for Boltzmann machines, augmenting each hidden unit with a latent transformation assignment variable which describes the selection of the transformed view of the canonical connection weights associated with the unit. This enables the inferences of the model to transform in response to transformed input data in a stable and predictable way, and avoids learning multiple features differing only with respect to the set of transformations. Extending prior work on translation equivariant (convolutional) models, we develop translation and rotation equivariant restricted Boltzmann machines (RBMs) and deep belief nets (DBNs), and demonstrate their effectiveness in learning frequently occurring statistical structure from artificial and natural images.
A Phasor Model with Resting States
, 2000
"... this paper. The model considered here wil be referred to as phasor model with resting states. ..."
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this paper. The model considered here wil be referred to as phasor model with resting states.
ObjectBased Attention and Occlusion: Evidence From Normal Participants and a Computational Model
"... One way of perceptually organizing a complex visual scene is to attend selectively to information in a particular physical location. Another way of reducing the complexity in the input is to attend selectively to an individual object in the scene and to process its elements preferentially. This latt ..."
Abstract
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One way of perceptually organizing a complex visual scene is to attend selectively to information in a particular physical location. Another way of reducing the complexity in the input is to attend selectively to an individual object in the scene and to process its elements preferentially. This latter, objectbased attention process was examined, and the predicted superiority for reporting features from 1 relative to 2 objects was replicated in a series of experiments. This objectbased process was robust even under conditions of occlusion, although there were some boundary conditions on its operation. Finally, an account of the data is provided via simulations of the findings in a computational model. The claim is that objectbased attention arises from a mechanism that groups together those features based on internal representations developed over perceptual experience and then preferentially gates these features for later, selective processing. Humans are exceptionally good at recognizing objects in natural visual scenes despite the fact that such scenes usually contain multiple, overlapping objects. One way in which individuals organize this complex input to minimize the processing load is to divide the field on the basis of spatial location and then to attend selectively to particular physical regions. This selective attentional spotlight "illuminates" areas of interest and facilitates preferential processing of information from those chosen areas (e.g., Broadbent, 1982;
Exponential family harmoniums with an . . .
"... Directed graphical models with one layer of observed random variables and one or more layers of hidden random variables have been the dominant modelling paradigm in many research fields. Although this approach has met with considerable success, the causal semantics of these models can make it diffic ..."
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Directed graphical models with one layer of observed random variables and one or more layers of hidden random variables have been the dominant modelling paradigm in many research fields. Although this approach has met with considerable success, the causal semantics of these models can make it difficult to infer the posterior distribution over the hidden variables. In this paper we propose an alternative twolayer model based on exponential family distributions and the semantics of undirected models. Inference in these âexponential family harmoniumsâ is fast while learning is performed by minimizing contrastive divergence. A member of this family is then studied as an alternative probabilistic model for latent semantic indexing. In experiments it is shown that they perform well on document retrieval tasks and provide an elegant solution to searching with keywords.
Compact modeling of highdimensional timeseries Contents
, 2007
"... Compact modeling of highdimensional timeseries ..."
LETTER Communicated by Marcelo Magnasco Phase Coupling Estimation from Multivariate Phase Statistics
"... Coupled oscillators are prevalent throughout the physical world. Dynamical system formulations of weakly coupled oscillator systems have proven effective at capturing the properties of realworld systems and are compelling models of neural systems. However, these formulations usually deal with the f ..."
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Coupled oscillators are prevalent throughout the physical world. Dynamical system formulations of weakly coupled oscillator systems have proven effective at capturing the properties of realworld systems and are compelling models of neural systems. However, these formulations usually deal with the forward problem: simulating a system from known coupling parameters. Here we provide a solution to the inverse problem: determining the coupling parameters from measurements. Starting from the dynamic equations of a system of symmetrically coupled phase oscillators, given by a nonlinear Langevin equation, we derive the corresponding equilibrium distribution. This formulation leads us to the maximum entropy distribution that captures pairwise phase relationships. To solve the inverse problem for this distribution, we derive a closedform solution for estimating the phase coupling parameters from observed phase statistics. Through simulations, we show that the algorithm performs well in high dimensions (d = 100) and in cases with limited data (as few as 100 samples per dimension). In addition, we derive a regularized solution to the estimation and show that the resulting procedure improves performance when only a limited amount of data is available. Because the distribution serves as the unique maximum entropy solution for pairwise phase statistics, phase coupling estimation can be broadly applied in any situation where phase measurements are made. Under the physical interpretation, the model may be used for inferring coupling relationships within cortical networks. 1
1 A Mixture Model For Population codes of Gabor Filters
"... Abstract—Population coding is a coding scheme which is ubiquitous in neural systems, and is also of more general use in coding stimuli, for example in vision problems. A population of responses to a stimulus can be used to represent not only the value of some variable in the environment, but a full ..."
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Abstract—Population coding is a coding scheme which is ubiquitous in neural systems, and is also of more general use in coding stimuli, for example in vision problems. A population of responses to a stimulus can be used to represent not only the value of some variable in the environment, but a full probability distribution for that variable. The information is held in a distributed and encoded form which may in some situations be more robust to noise and failures than conventional representations. Gabor filters are a popular choice for detecting edges in the visual field for several reasons. They are easily tuned for a variety of edge widths and orientations, and are considered a close model of the edge filters in the human visual system. In this paper we consider population codes of Gabor filters with different orientations. A probabilistic model of Gabor filter responses is presented. Based on the analytically derived orientation tuning function and a parametric mixture model of the filter responses in the presence of local edge structure with single or multiple orientations a probability density function of the local orientation in any point (x, y) can be extracted through a parameter estimation procedure. The resulting pdf of the local contour orientation captures not only angular information at edges, corners or Tjunctions but also describes the certainty of the measurement which can be characterized in terms of the entropy of the individual mixture components.