Results 1  10
of
282
A Unifying Review of Linear Gaussian Models
, 1999
"... Factor analysis, principal component analysis, mixtures of gaussian clusters, vector quantization, Kalman filter models, and hidden Markov models can all be unified as variations of unsupervised learning under a single basic generative model. This is achieved by collecting together disparate observa ..."
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Cited by 263 (17 self)
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Factor analysis, principal component analysis, mixtures of gaussian clusters, vector quantization, Kalman filter models, and hidden Markov models can all be unified as variations of unsupervised learning under a single basic generative model. This is achieved by collecting together disparate observations and derivations made by many previous authors and introducing a new way of linking discrete and continuous state models using a simple nonlinearity. Through the use of other nonlinearities, we show how independent component analysis is also a variation of the same basic generative model. We show that factor analysis and mixtures of gaussians can be implemented in autoencoder neural networks and learned using squared error plus the same regularization term. We introduce a new model for static data, known as sensible principal component analysis, as well as a novel concept of spatially adaptive observation noise. We also review some of the literature involving global and local mixtures of the basic models and provide pseudocode for inference and learning for all the basic models.
Dynamic Model of Visual Recognition Predicts Neural Response Properties in the Visual Cortex
 Neural Computation
, 1995
"... this paper, we describe a hierarchical network model of visual recognition that explains these experimental observations by using a form of the extended Kalman filter as given by the Minimum Description Length (MDL) principle. The model dynamically combines inputdriven bottomup signals with expec ..."
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Cited by 86 (21 self)
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this paper, we describe a hierarchical network model of visual recognition that explains these experimental observations by using a form of the extended Kalman filter as given by the Minimum Description Length (MDL) principle. The model dynamically combines inputdriven bottomup signals with expectationdriven topdown signals to predict current recognition state. Synaptic weights in the model are adapted in a Hebbian manner according to a learning rule also derived from the MDL principle. The resulting prediction/learning scheme can be viewed as implementing a form of the ExpectationMaximization (EM) algorithm. The architecture of the model posits an active computational role for the reciprocal connections between adjoining visual cortical areas in determining neural response properties. In particular, the model demonstrates the possible role of feedback from higher cortical areas in mediating neurophysiological effects due to stimuli from beyond the classical receptive field. Si
An Intelligent Predictive Control Approach to the HighSpeed CrossCountry Autonomous Navigation Problem
, 1995
"... mRIm9533 submitted in partial fulfiumtnr of the reqimlmts for the degm of ..."
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Cited by 72 (3 self)
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mRIm9533 submitted in partial fulfiumtnr of the reqimlmts for the degm of
Measurement and Integration of 3D Structures by Tracking Edge Lines
, 1992
"... This paper describes techniques for dynamically modeling the 2D appearance and 3D geometry of a scene by integrating information from a moving camera. These techniques are illustrated by the design of a system which constructs a geometric description of a scene from the motion of a camera mounted ..."
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Cited by 54 (6 self)
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This paper describes techniques for dynamically modeling the 2D appearance and 3D geometry of a scene by integrating information from a moving camera. These techniques are illustrated by the design of a system which constructs a geometric description of a scene from the motion of a camera mounted on a robot arm. A framework
Detection of Stochastic Processes
 IEEE Trans. Inform. Theory
, 1998
"... This paper reviews two streams of development, from the 1940's to the present, in signal detection theory: the structure of the likelihood ratio for detecting signals in noise and the role of dynamic optimization in detection problems involving either very large signal sets or the joint optimization ..."
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Cited by 40 (6 self)
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This paper reviews two streams of development, from the 1940's to the present, in signal detection theory: the structure of the likelihood ratio for detecting signals in noise and the role of dynamic optimization in detection problems involving either very large signal sets or the joint optimization of observation time and performance. This treatment deals exclusively with basic results developed for the situation in which the observations are modeled as continuoustime stochastic processes. The mathematics and intuition behind such developments as the matched filter, the RAKE receiver, the estimatorcorrelator, maximumlikelihood sequence detectors, multiuser detectors, sequential probability ratio tests, and cumulativesum quickest detectors, are described. Index Terms Dynamic programming, innovations processes, likelihood ratios, martingale theory, matched filters, optimal stopping, reproducing kernel Hilbert spaces, sequence detection, sequential methods, signal detection, signal estimation.
An optimal estimation approach to visual perception and learning
 VISION RESEARCH
, 1999
"... How does the visual system learn an internal model of the external environment? How is this internal model used during visual perception? How are occlusions and background clutter so effortlessly discounted for when recognizing a familiar object? How is a particular object of interest attended to an ..."
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Cited by 39 (8 self)
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How does the visual system learn an internal model of the external environment? How is this internal model used during visual perception? How are occlusions and background clutter so effortlessly discounted for when recognizing a familiar object? How is a particular object of interest attended to and recognized in the presence of other objects in the field of view? In this paper, we attempt to address these questions from the perspective of Bayesian optimal estimation theory. Using the concept of generative models and the statistical theory of Kalman filtering, we show how static and dynamic events occurring in the visual environment may be learned and recognized given only the input images. We also describe an extension of the Kalman filter model that can handle multiple objects in the field of view. The resulting robust Kalman filter model demonstrates how certain forms of attention can be viewed as an emergent property of the interaction between top–down expectations and bottom–up signals. Experimental results are provided to help demonstrate the ability of such a model to perform robust segmentation and recognition of objects and image sequences in the presence of occlusions and clutter.
Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter
 Physica D
, 2007
"... Data assimilation is an iterative approach to the problem of estimating the state of a dynamical system using both current and past observations of the system together with a model for the system’s time evolution. Rather than solving the problem from scratch each time new observations become availab ..."
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Cited by 33 (5 self)
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Data assimilation is an iterative approach to the problem of estimating the state of a dynamical system using both current and past observations of the system together with a model for the system’s time evolution. Rather than solving the problem from scratch each time new observations become available, one uses the model to “forecast ” the current state, using a prior state estimate (which incorporates information from past data) as the initial condition, then uses current data to correct the prior forecast to a current state estimate. This Bayesian approach is most effective when the uncertainty in both the observations and in the state estimate, as it evolves over time, are accurately quantified. In this article, I describe a practical method for data assimilation in large, spatiotemporally chaotic systems. The method is a type of “Ensemble Kalman Filter”, in which the state estimate and its approximate uncertainty are represented at any given time by an ensemble of system states. I discuss both the mathematical basis of this approach and its implementation; my primary emphasis is on ease of use and computational speed rather than improving accuracy over previously published approaches to ensemble Kalman filtering. 1
An Overview of Nonlinear Model Predictive Control Applications
 Nonlinear Predictive Control
, 2000
"... . This paper provides an overview of nonlinear model predictive control (NMPC) applications in industry, focusing primarily on recent applications reported by NMPC vendors. A brief summary of NMPC theory is presented to highlight issues pertinent to NMPC applications. Five industrial NMPC implem ..."
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Cited by 31 (1 self)
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. This paper provides an overview of nonlinear model predictive control (NMPC) applications in industry, focusing primarily on recent applications reported by NMPC vendors. A brief summary of NMPC theory is presented to highlight issues pertinent to NMPC applications. Five industrial NMPC implementations are then discussed with reference to modeling, control, optimization, and implementation issues. Results from several industrial applications are presented to illustrate the benefits possible with NMPC technology. A discussion of future needs in NMPC theory and practice is provided to conclude the paper. 1. Introduction The term Model Predictive Control (MPC) describes a class of computer control algorithms that control the future behavior of a plant through the use of an explicit process model. At each control interval the MPC algorithm computes an openloop sequence of manipulated variable adjustments in order to optimize future plant behavior. The first input in the optima...
etc. Securing Internet Coordinate Embedding Systems
 In Proceedings of the ACM SIGCOMM
, 2007
"... This paper addresses the issue of the security of Internet Coordinate Systems, by proposing a general method for malicious behavior detection during coordinate computations. We first show that the dynamics of a node, in a coordinate system without abnormal or malicious behavior, can be modeled by a ..."
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Cited by 29 (4 self)
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This paper addresses the issue of the security of Internet Coordinate Systems, by proposing a general method for malicious behavior detection during coordinate computations. We first show that the dynamics of a node, in a coordinate system without abnormal or malicious behavior, can be modeled by a Linear State Space model and tracked by a Kalman filter. Then we show that the obtained model can be generalized in the sense that the parameters of a filter calibrated at a node can be used effectively to model and predict the dynamic behavior at another node, as long as the two nodes are not too far apart in the network. This leads to the proposal of a Surveyor infrastructure: Surveyor nodes are trusted, honest nodes that use each other exclusively to position themselves in the coordinate space, and are therefore immune to malicious behavior in the system. During their own coordinate embedding, other nodes can then use the filter parameters of a nearby Surveyor as a representation of normal, clean system behavior to detect and filter out abnormal or malicious activity. A combination of simulations and PlanetLab experiments are used to demonstrate the validity, generality, and effectiveness of the proposed approach for two representative coordinate embedding systems, namely Vivaldi and NPS.
Principles and Techniques for Sensor Data Fusion
, 1993
"... This paper concerns a problem which is basic to perception: the integration of perceptual information into a coherent description of the world. In this paper we present perception as a process of dynamically maintaining a model of the local external environment. Fusion of perceptual information is a ..."
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Cited by 29 (6 self)
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This paper concerns a problem which is basic to perception: the integration of perceptual information into a coherent description of the world. In this paper we present perception as a process of dynamically maintaining a model of the local external environment. Fusion of perceptual information is at the heart of this process.