Abstract We propose two estimation and updating schemes which use infinitesimal perturbation anal... more Abstract We propose two estimation and updating schemes which use infinitesimal perturbation analysis based derivative estimates for the recursive optimization of queues. With the aid of extensions of convergence theorems from stochastic approximation, we prove convergence of the two proposed algorithms, when applied to an M/G/1 queue and to a multi-queue system. We also present simulation results illustrating our theorems for the case of an M/M/1 queue, and a three-queue system.
Advanced brain imaging analysis methods, including multivariate pattern analysis (MVPA), function... more Advanced brain imaging analysis methods, including multivariate pattern analysis (MVPA), functional connectivity, and functional alignment, have become powerful tools in cognitive neuroscience over the past decade. These tools are implemented in custom code and separate packages, often requiring different software and language proficiencies to deploy for data analysis. Although usable by expert researchers, novice users face a steep learning curve. These difficulties stem from the use of new programming languages (e.g., Python), learning how to apply machine-learning methods to high-dimensional fMRI data; and minimal documentation and training materials. Furthermore, most standard fMRI analysis packages (e.g., AFNI, FSL, SPM) focus on preprocessing and univariate analyses, leaving a gap in how to integrate with advanced tools. To address these needs, we developed BrainIAK (brainiak.org), an open-source Python software package that seamlessly integrates several cutting-edge, computat...
Analysis methods in cognitive neuroscience have not always matched the richness of fMRI data. Ear... more Analysis methods in cognitive neuroscience have not always matched the richness of fMRI data. Early methods focused on estimating neural activity within individual voxels or regions, averaged over trials or blocks and modeled separately in each participant. This approach mostly neglected the distributed nature of neural representations over voxels, the continuous dynamics of neural activity during tasks, the statistical benefits of performing joint inference over multiple participants and the value of using predictive models to constrain analysis. Several recent exploratory and theory-driven methods have begun to pursue these opportunities. These methods highlight the importance of computational techniques in fMRI analysis, especially machine learning, algorithmic optimization and parallel computing. Adoption of these techniques is enabling a new generation of experiments and analyses that could transform our understanding of some of the most complex-and distinctly human-signals in ...
Acoustics Speech and Signal Processing 1988 Icassp 88 1988 International Conference on, Apr 19, 2009
Diffusion Maps (DiffMaps) has recently provided a general framework that unites many other spectr... more Diffusion Maps (DiffMaps) has recently provided a general framework that unites many other spectral manifold learning algorithms, including Laplacian Eigenmaps, and it has become one of the most successful and popular frameworks for manifold learning to date. However, Diffusion Maps still often creates unnecessary distortions, and its performance varies widely in response to parameter value changes. In this paper, we draw a previously unnoticed connection between DiffMaps and spring-motivated methods. We show that DiffMaps has a physical interpretation: it finds the arrangement of high-dimensional objects in low-dimensional space that minimizes the elastic energy of a particular spring network. Within this interpretation, we recognize the root cause of a variety of problems that are commonly observed in the Diffusion Maps output, including sensitivity to user-specified parameters, sensitivity to sampling density, and distortion of boundaries. We then show how to exploit the connection between Diffusion Map and spring criteria to create a method that can be efficiently applied post hoc to alleviate these commonly observed deficiencies in the Diffusion Maps output.
IEEE transactions on pattern analysis and machine intelligence, May 12, 2016
This paper is a survey of dictionary screening for the lasso problem. The lasso problem seeks a s... more This paper is a survey of dictionary screening for the lasso problem. The lasso problem seeks a sparse linear combination of the columns of a dictionary to best match a given target vector. This sparse representation has proven useful in a variety of subsequent processing and decision tasks. For a given target vector, dictionary screening quickly identifies a subset of dictionary columns that will receive zero weight in a solution of the corresponding lasso problem. These columns can be removed from the dictionary prior to solving the lasso problem without impacting the optimality of the solution obtained. This has two potential advantages: it reduces the size of the dictionary, allowing the lasso problem to be solved with less resources, and it may speed up obtaining a solution. Using a geometrically intuitive framework, we provide basic insights for understanding useful lasso screening tests and their limitations. We also provide illustrative numerical studies on several datasets.
Siam Journal on Control and Optimization, Feb 17, 2012
... DISCRETE TIME STOCHASTIC ADAPTIVE CONTROL ... It is only recently that significant progress h... more ... DISCRETE TIME STOCHASTIC ADAPTIVE CONTROL ... It is only recently that significant progress has been made on the global convergence of adaptive control algorithms. ...
Lecture Notes in Control and Information Sciences, 1998
ABSTRACT We analyse a switching control system for controlling a plant with unknown parameters so... more ABSTRACT We analyse a switching control system for controlling a plant with unknown parameters so that the output asymptotically tracks a reference signal. The controller is selected on-line from a given set of controllers according to a switching rule based on output prediction errors. We provide sufficient conditions under which the switched closed loop control system is exponentially stable and asymptotically achieves good tracking control even if the switching does not stop.
Acoustics Speech and Signal Processing 1988 Icassp 88 1988 International Conference on, Mar 14, 2010
ABSTRACT We propose a new approach to semi-supervised clustering that utilizes boosting to simult... more ABSTRACT We propose a new approach to semi-supervised clustering that utilizes boosting to simultaneously learn both a similarity measure and a clustering of the data from given instance-level must-link and cannot-link constraints. The approach is distinctive in that it uses a supervising feedback loop to gradually update the similarity while at the same time guiding an underlying unsupervised clustering algorithm. Our approach is grounded in the theory of boosting. We provide three examples of the clustering algorithm on real datasets.
Cerebral cortex (New York, N.Y. : 1991), Jun 14, 2016
Current models of the functional architecture of human cortex emphasize areas that capture coarse... more Current models of the functional architecture of human cortex emphasize areas that capture coarse-scale features of cortical topography but provide no account for population responses that encode information in fine-scale patterns of activity. Here, we present a linear model of shared representational spaces in human cortex that captures fine-scale distinctions among population responses with response-tuning basis functions that are common across brains and models cortical patterns of neural responses with individual-specific topographic basis functions. We derive a common model space for the whole cortex using a new algorithm, searchlight hyperalignment, and complex, dynamic stimuli that provide a broad sampling of visual, auditory, and social percepts. The model aligns representations across brains in occipital, temporal, parietal, and prefrontal cortices, as shown by between-subject multivariate pattern classification and intersubject correlation of representational geometry, ind...
Abstract We propose two estimation and updating schemes which use infinitesimal perturbation anal... more Abstract We propose two estimation and updating schemes which use infinitesimal perturbation analysis based derivative estimates for the recursive optimization of queues. With the aid of extensions of convergence theorems from stochastic approximation, we prove convergence of the two proposed algorithms, when applied to an M/G/1 queue and to a multi-queue system. We also present simulation results illustrating our theorems for the case of an M/M/1 queue, and a three-queue system.
Advanced brain imaging analysis methods, including multivariate pattern analysis (MVPA), function... more Advanced brain imaging analysis methods, including multivariate pattern analysis (MVPA), functional connectivity, and functional alignment, have become powerful tools in cognitive neuroscience over the past decade. These tools are implemented in custom code and separate packages, often requiring different software and language proficiencies to deploy for data analysis. Although usable by expert researchers, novice users face a steep learning curve. These difficulties stem from the use of new programming languages (e.g., Python), learning how to apply machine-learning methods to high-dimensional fMRI data; and minimal documentation and training materials. Furthermore, most standard fMRI analysis packages (e.g., AFNI, FSL, SPM) focus on preprocessing and univariate analyses, leaving a gap in how to integrate with advanced tools. To address these needs, we developed BrainIAK (brainiak.org), an open-source Python software package that seamlessly integrates several cutting-edge, computat...
Analysis methods in cognitive neuroscience have not always matched the richness of fMRI data. Ear... more Analysis methods in cognitive neuroscience have not always matched the richness of fMRI data. Early methods focused on estimating neural activity within individual voxels or regions, averaged over trials or blocks and modeled separately in each participant. This approach mostly neglected the distributed nature of neural representations over voxels, the continuous dynamics of neural activity during tasks, the statistical benefits of performing joint inference over multiple participants and the value of using predictive models to constrain analysis. Several recent exploratory and theory-driven methods have begun to pursue these opportunities. These methods highlight the importance of computational techniques in fMRI analysis, especially machine learning, algorithmic optimization and parallel computing. Adoption of these techniques is enabling a new generation of experiments and analyses that could transform our understanding of some of the most complex-and distinctly human-signals in ...
Acoustics Speech and Signal Processing 1988 Icassp 88 1988 International Conference on, Apr 19, 2009
Diffusion Maps (DiffMaps) has recently provided a general framework that unites many other spectr... more Diffusion Maps (DiffMaps) has recently provided a general framework that unites many other spectral manifold learning algorithms, including Laplacian Eigenmaps, and it has become one of the most successful and popular frameworks for manifold learning to date. However, Diffusion Maps still often creates unnecessary distortions, and its performance varies widely in response to parameter value changes. In this paper, we draw a previously unnoticed connection between DiffMaps and spring-motivated methods. We show that DiffMaps has a physical interpretation: it finds the arrangement of high-dimensional objects in low-dimensional space that minimizes the elastic energy of a particular spring network. Within this interpretation, we recognize the root cause of a variety of problems that are commonly observed in the Diffusion Maps output, including sensitivity to user-specified parameters, sensitivity to sampling density, and distortion of boundaries. We then show how to exploit the connection between Diffusion Map and spring criteria to create a method that can be efficiently applied post hoc to alleviate these commonly observed deficiencies in the Diffusion Maps output.
IEEE transactions on pattern analysis and machine intelligence, May 12, 2016
This paper is a survey of dictionary screening for the lasso problem. The lasso problem seeks a s... more This paper is a survey of dictionary screening for the lasso problem. The lasso problem seeks a sparse linear combination of the columns of a dictionary to best match a given target vector. This sparse representation has proven useful in a variety of subsequent processing and decision tasks. For a given target vector, dictionary screening quickly identifies a subset of dictionary columns that will receive zero weight in a solution of the corresponding lasso problem. These columns can be removed from the dictionary prior to solving the lasso problem without impacting the optimality of the solution obtained. This has two potential advantages: it reduces the size of the dictionary, allowing the lasso problem to be solved with less resources, and it may speed up obtaining a solution. Using a geometrically intuitive framework, we provide basic insights for understanding useful lasso screening tests and their limitations. We also provide illustrative numerical studies on several datasets.
Siam Journal on Control and Optimization, Feb 17, 2012
... DISCRETE TIME STOCHASTIC ADAPTIVE CONTROL ... It is only recently that significant progress h... more ... DISCRETE TIME STOCHASTIC ADAPTIVE CONTROL ... It is only recently that significant progress has been made on the global convergence of adaptive control algorithms. ...
Lecture Notes in Control and Information Sciences, 1998
ABSTRACT We analyse a switching control system for controlling a plant with unknown parameters so... more ABSTRACT We analyse a switching control system for controlling a plant with unknown parameters so that the output asymptotically tracks a reference signal. The controller is selected on-line from a given set of controllers according to a switching rule based on output prediction errors. We provide sufficient conditions under which the switched closed loop control system is exponentially stable and asymptotically achieves good tracking control even if the switching does not stop.
Acoustics Speech and Signal Processing 1988 Icassp 88 1988 International Conference on, Mar 14, 2010
ABSTRACT We propose a new approach to semi-supervised clustering that utilizes boosting to simult... more ABSTRACT We propose a new approach to semi-supervised clustering that utilizes boosting to simultaneously learn both a similarity measure and a clustering of the data from given instance-level must-link and cannot-link constraints. The approach is distinctive in that it uses a supervising feedback loop to gradually update the similarity while at the same time guiding an underlying unsupervised clustering algorithm. Our approach is grounded in the theory of boosting. We provide three examples of the clustering algorithm on real datasets.
Cerebral cortex (New York, N.Y. : 1991), Jun 14, 2016
Current models of the functional architecture of human cortex emphasize areas that capture coarse... more Current models of the functional architecture of human cortex emphasize areas that capture coarse-scale features of cortical topography but provide no account for population responses that encode information in fine-scale patterns of activity. Here, we present a linear model of shared representational spaces in human cortex that captures fine-scale distinctions among population responses with response-tuning basis functions that are common across brains and models cortical patterns of neural responses with individual-specific topographic basis functions. We derive a common model space for the whole cortex using a new algorithm, searchlight hyperalignment, and complex, dynamic stimuli that provide a broad sampling of visual, auditory, and social percepts. The model aligns representations across brains in occipital, temporal, parietal, and prefrontal cortices, as shown by between-subject multivariate pattern classification and intersubject correlation of representational geometry, ind...
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Papers by Peter j. Ramadge