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16th Annual Neural Information Processing Systems Conference, NIPS 2002

作者： Zoran, Daniel Weiss, Yair Interdisciplinary Center for Neural Computation Hebrew University of Jerusalem Israel School of Computer Science and Engineering Hebrew University of Jerusalem Israel

ISBN: (纸本)0262025507

Simple Gaussian Mixture Models (GMMs) learned from pixels of natural image patches have been recently shown to be surprisingly strong performers in modeling the statistics of natural images. Here we provide an in depth analysis of this simple yet rich model. We show that such a GMM model is able to compete with even the most successful models of natural images in log likelihood scores, denoising performance and sample quality. We provide an analysis of what such a model learns from natural images as a function of number of mixture components - including covariance structure, contrast variation and intricate structures such as textures, boundaries and more. Finally, we show that the salient properties of the GMM learned from natural images can be derived from a simplified Dead Leaves model which explicitly models occlusion, explaining its surprising success relative to other models.

关键词： computer vision

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A pattern matching algorithm for codon optimization and CpG motif-engineering in DNA expression vectors 2

A pattern matching algorithm for codon optimization and CpG ...

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2nd International IEEE computer Society computational Systems Bioinformatics Conference, CSB 2003

作者： Satya, Ravi Vijaya Mukherjee, Amar Ranga, Udaykumar School of Engineering and Computer Science University of Central Florida OrlandoFL32816 United States Jawaharlal Nehru Center for Advanced Scientific Research Jakkur Bangalore India

ISBN: (纸本)0769520006

Codon optimization enhances the efficiency of DNA expression vectors used in DNA vaccination and gene therapy by increasing protein expression. Additionally, certain nucleotide motifs have experimentally been shown to be immuno-stimulatory while certain others immuno-suppressive. In this paper, we present algorithms to locate a given set of immuno-modulatory motifs in the DNA expression vectors corresponding to a given amino acid sequence and maximize or minimize the number and the context of the immuno-modulatory motifs in the DNA expression vectors. The main contribution is to use multiple pattern matching algorithms to synthesize a DNA sequence for a given amino acid sequence and a graph theoretic approach for finding the longest weighted path in a directed graph that will maximize or minimize certain motifs. This is achieved using O(n2) time, where n is the length of the amino acid sequence. Based on this, we develop a software tool. © 2003 IEEE.

关键词： Gene therapy

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Sufficient dimensionality reduction

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Journal of Machine Learning Research 2003年 3卷 1307-1331页

作者： Globerson, Amir Tishby, Naftali School of Computer Science and Engineering Interdisciplinary Center for Neural Computation Hebrew University Jerusalem 91904 Israel

Dimensionality reduction of empirical co-occurrence data is a fundamental problem in unsupervised learning. It is also a well studied problem in statistics known as the analysis of cross-classified data. One principled approach to this problem is to represent the data in low dimension with minimal loss of (mutual) information contained in the original data. In this paper we introduce an information theoretic nonlinear method for finding such a most informative dimension reduction. In contrast with previously introduced clustering based approaches, here we extract continuous feature functions directly from the co-occurrencematrix. In a sense, we automatically extract functions of the variables that serve as approximate sufficient statistics for a sample of one variable about the other one. Our method is different from dimensionality reduction methods which are based on a specific, sometimes arbitrary, metric or embedding. Another interpretation of our method is as generalized - multi-dimensional - non-linear regression, where rather than fitting one regression function through two dimensional data, we extract d-regression functions whose expectation values capture the information among the variables. It thus presents a new learning paradigm that unifies aspects from both supervised and unsupervised learning. The resulting dimension reduction can be described by two conjugate d-dimensional differential manifolds that are coupled through Maximum Entropy I-projections. The Riemannian metrics of these manifolds are determined by the observed expectation values of our extracted features. Following this geometric interpretation we present an iterative information projection algorithm for finding such features and prove its convergence. Our algorithm is similar to the method of "association analysis" in statistics, though the feature extraction context as well as the information theoretic and geometric interpretation are new. The algorithm is illustrated by various syntheti

关键词： Feature extraction

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Spikerneis: Embedding spiking neurons in inner-product spaces

Spikerneis: Embedding spiking neurons in inner-product space...

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16th Annual Neural Information Processing Systems Conference, NIPS 2002

作者： Shpigelman, Lavi Singer, Yoram Paz, Rony Vaadia, Eilon School of Computer Science and Engineering Hebrew University Jerusalem 91904 Israel Interdisciplinary Center for Neural Computation Hebrew University Jerusalem 91904 Israel Dept. of Physiology Hadassah Medical School Hebrew University Jerusalem 91904 Israel

ISBN: (纸本)0262025507

Inner-product operators, often referred to as kernels in statistical learning, define a mapping from some input space into a feature space. The focus of this paper is the construction of biologically-motivated kernels for cortical activities. The kernels we derive, termed Spikernels, map spike count sequences into an abstract vector space in which we can perform various prediction tasks. We discuss in detail the derivation of Spikernels and describe an efficient algorithm for computing their value on any two sequences of neural population spike counts. We demonstrate the merits of our modeling approach using the Spikernel and various standard kernels for the task of predicting hand movement velocities from cortical recordings. In all of our experiments all the kernels we tested outperform the standard scalar product used in regression with the Spikernel consistently achieving the best performance.

关键词： Vector spaces

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Information bottleneck for Gaussian variables 03

Information bottleneck for Gaussian variables

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Proceedings of the 17th International Conference on Neural Information Processing Systems

作者： Gal Chechik Amir Globerson Naftali Tishby Yair Weiss School of Computer Science and Engineering and The Interdisciplinary Center for Neural Computation The Hebrew University of Jerusalem Israel

The problem of extracting the relevant aspects of data was addressed through the information bottleneck (IB) method, by (soft) clustering one variable while preserving information about another - relevance - variable. An interesting question addressed in the current work is the extension of these ideas to obtain continuous representations that preserve relevant information, rather than discrete clusters. We give a formal definition of the general continuous IB problem and obtain an analytic solution for the optimal representation for the important case of multivariate Gaussian variables. The obtained optimal representation is a noisy linear projection to eigenvectors of the normalized correlation matrix ∑x|y∑−1x, which is also the basis obtained in Canonical Correlation Analysis. However, in Gaussian IB, the compression tradeoff parameter uniquely determines the dimension, as well as the scale of each eigenvector. This introduces a novel interpretation where solutions of different ranks lie on a continuum parametrized by the compression level. Our analysis also provides an analytic expression for the optimal tradeoff - the information curve - in terms of the eigenvalue spectrum.

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Enhancing image and video retrieval: learning via equivalence constraints

Enhancing image and video retrieval: learning via equivalenc...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： T. Hertz N. Shental A. Bar-Hillel D. Weinshall School of Computer Science and Engineering and the Center for Neural Computation Hebrew University of Jerusalem Jerusalem Israel

The paper is about learning using partial information in the form of equivalence constraints. Equivalence constraints provide relational information about the labels of data points, rather than the labels themselves. Our work is motivated by the observation that in many real life applications partial information about the data can be obtained with very little cost. For example, in video indexing we may want to use the fact that a sequence of faces obtained from successive frames in roughly the same location is likely to contain the same unknown individual. Learning using equivalence constraints is different from learning using labels and poses new technical challenges. In this paper we present three novel methods for clustering and classification, which use equivalence constraints. We provide results of our methods on a distributed image querying system that works on a large facial image database, and on the clustering and retrieval of surveillance data. Our results show that we can significantly improve the performance of image retrieval by taking advantage of such assumptions as temporal continuity in the data. Significant improvement is also obtained by making the users of the system take the role of distributed teachers, which reduces the need for expensive labeling by paid human labor.

关键词： Image retrieval Information retrieval Indexing Surveillance Humans Training data Data mining computer science Costs Image databases

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Distributional word clusters vs. words for text categorization

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Journal of Machine Learning Research 2003年 3卷 1183-1208页

作者： Bekkerman, Ron El-Yaniv, Ran Tishby, Naftali Winter, Yoad Department of Computer Science Technion - Israel Institute of Technology Haifa 32000 Israel School of Computer Science and Engineering Center for Neural Computation Hebrew University Jerusalem 91904 Israel

We study an approach to text categorization that combines distributional clustering of words and a Support Vector Machine (SVM) classifier. This word-cluster representation is computed using the recently introduced Information Bottleneck method, which generates a compact and efficient representation of documents. When combined with the classification power of the SVM, this method yields high performance in text categorization. This novel combination of SVM with word-cluster representation is compared with SVM-based categorization using the simpler bag-of-words (BOW) representation. The comparison is performed over three known datasets. On one of these datasets (the 20 Newsgroups) the method based on word clusters significantly outperforms the word-based representation in terms of categorization accuracy or representation efficiency. On the two other sets (Reuters-21578 and WebKB) the word-based representation slightly outperforms the word-cluster representation. We investigate the potential reasons for this behavior and relate it to structural differences between the datasets. © 2003 Ron Bekkerman, Ran El-Yaniv, Naftali Tishby, and Yoad Winter.

关键词： Support vector machines

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A pattern matching algorithm for codon optimization and CpG motif-engineering in DNA expression vectors

A pattern matching algorithm for codon optimization and CpG ...

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IEEE Conference on Bioinformatics

作者： Ravi Vijaya Satya Amar Mukherjee Udaykumar Ranga School of Engineering and Computer Science University of Central Florida Orlando FL USA Jawaharlal Nehru Center for Advanced Scientific Research Bangalore India

ISBN: (纸本)0769520006

Codon optimization enhances the efficiency of DNA expression vectors used in DNA vaccination and gene therapy by increasing protein expression. Additionally, certain nucleotide motifs have experimentally been shown to be immuno-stimulatory while certain others immuno-suppressive. In this paper, we present algorithms to locate a given set of immuno-modulatory motifs in the DNA expression vectors corresponding to a given amino acid sequence and maximize or minimize the number and the context of the immuno-modulatory motifs in the DNA expression vectors. The main contribution is to use multiple pattern matching algorithms to synthesize a DNA sequence for a given amino acid sequence and a graph theoretic approach for finding the longest weighted path in a directed graph that will maximize or minimize certain motifs. This is achieved using O(n/sup 2/) time, where n is the length of the amino acid sequence. Based on this, we develop a software tool.

关键词： Pattern matching DNA Immune system Vaccines Proteins Sequences Animals Amino acids Humans Pathogens

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Identifying and exploiting spatial regularity in data memory references 03

Identifying and exploiting spatial regularity in data memory...

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2003 ACM/IEEE Conference on Supercomputing, SC 2003

作者： Mohan, Tushar De Supinski, Bronis R. McKee, Sally A. Mueller, Frank Yoo, Andy Schulz, Martin Lawrence Berkeley National Laboratory Future Technologies Group Berkeley CA 94720 United States Lawrence Livermore National Laboratory Center for Applied Scientific Computing Livermore CA 94551 United States School of Electrical and Computer Engineering Cornell University Ithaca NY 14853 United States Department of Computer Science North Carolina State University Raleigh NC 27695-7534 United States

ISBN: (纸本)1581136951

The growing processor/memory performance gap causes the performance of many codes to be limited by memory accesses. If known to exist in an application, strided memory accesses forming streams can be targeted by optimizations such as prefetching, relocation, remapping, and vector loads. Undetected, they can be a significant source of memory stalls in loops. Existing stream-detection mechanisms either require special hardware, which may not gather statistics for subsequent analysis, or are limited to compile-time detection of array accesses in loops. Formally, little treatment has been accorded to the subject;the concept of locality fails to capture the existence of streams in a program's memory accesses. The contributions of this paper are as follows. First, we define spatial regularity as a means to discuss the presence and effects of streams. Second, we develop measures to quantify spatial regularity, and we design and implement an on-line, parallel algorithm to detect streams - and hence regularity - in running applications. Third, we use examples from real codes and common benchmarks to illustrate how derived stream statistics can be used to guide the application of profile-driven optimizations. Overall, we demonstrate the benefits of our novel regularity metric as an instrument to detect potential for code optimizations affecting memory performance. © 2003 ACM.

关键词： Codes (symbols)

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Sufficient dimensionality reduction

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2003年 3卷

作者： Amir Globerson Naftali Tishby School of Computer Science and Engineering and The Interdisciplinary Center for Neural Computation The Hebrew University Jerusalem 91904 Israel

Dimensionality reduction of empirical co-occurrence data is a fundamental problem in unsupervised learning. It is also a well studied problem in statistics known as the analysis of cross-classified data. One principled approach to this problem is to represent the data in low dimension with minimal loss of (mutual) information contained in the original data. In this paper we introduce an information theoretic nonlinear method for finding such a most informative dimension reduction. In contrast with previously introduced clustering based approaches, here we extract continuous feature functions directly from the co-occurrence matrix. In a sense, we automatically extract functions of the variables that serve as approximate sufficient statistics for a sample of one variable about the other one. Our method is different from dimensionality reduction methods which are based on a specific, sometimes arbitrary, metric or embedding. Another interpretation of our method is as generalized - multi-dimensional - non-linear regression, where rather than fitting one regression function through two dimensional data, we extract d -regression functions whose expectation values capture the information among the variables. It thus presents a new learning paradigm that unifies aspects from both supervised and unsupervised learning. The resulting dimension reduction can be described by two conjugate d-dimensional differential manifolds that are coupled through Maximum Entropy I -projections. The Riemannian metrics of these manifolds are determined by the observed expectation values of our extracted features. Following this geometric interpretation we present an iterative information projection algorithm for finding such features and prove its convergence. Our algorithm is similar to the method of "association analysis" in statistics, though the feature extraction context as well as the information theoretic and geometric interpretation are new. The algorithm is illustrated by various synth

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