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2004 IEEE Information Theory Workshop - Proceedings, ITW

作者： Shental, Ori Weiss, Anthony J. Shental, Noam Weiss, Yair Faculty of Engineering Dept. of Electrical Eng.-Systems Tel Aviv University Tel Aviv 69978 Israel Computer Science and Eng Center for Neural Computation Hebrew University of Jerusalem Jerusalem 91904 Israel

ISBN: (纸本)0780387201

We propose a generalized belief propagation (GBP) receiver for two-dimensional (2-D) channels with memory, which is applicative to 2-D intersymbol interference (ISI) equalization and multi-user detection (MUD). Our experimental study demonstrates that under non-trivial interference conditions, the performance of this fully tractable GBP receiver is almost identical to the performance of the optimal maximum a-posteriori (MAP) receiver. © 2004 IEEE.

关键词： Signal receivers

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Learning From a Small Number of Training Examples by Exploiting Object Categories

Learning From a Small Number of Training Examples by Exploit...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： K. Levi M. Fink Y. Weiss School of Computer Science and Engineering Hebrew University of Jerusalem Jerusalem Israel Center for Neural Computation Hebrew University of Jerusalem Jerusalem Israel

In the last few years, object detection techniques have progressed immensely. Impressive detection results have been achieved for many objects such as faces [11, 14, 9] and cars [11]. The robustness of these systems emerges from a training stage utilizing thousands of positive examples. One approach to enable learning from a small set of training examples is to find an efficient set of features that accurately represent the target object. Unfortunately, automatically selecting such a feature set is a difficult task in itself. In this paper we present a novel feature selection method that is based on the notion of object categories. We assume that when learning to recognize a new object (like an apple) we also know a category it belongs to (fruit). We further assume that features that are useful for learning other objects in the same category (e.g. pear or orange) will also be useful for learning the novel object. This leads to a simple criterion for selecting features and building classifiers. We show that our method gives significant improvement in detection performance in challenging domains.

关键词： Object detection Face detection computer science Robustness Humans computer vision computer displays Switches computer Society Pattern recognition

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A temporal kernel-based model for tracking hand-movements from neural activities 04

A temporal kernel-based model for tracking hand-movements fr...

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Proceedings of the 18th International Conference on neural Information Processing Systems

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

We devise and experiment with a dynamical kernel-based system for tracking hand movements from neural activity. The state of the system corresponds to the hand location, velocity, and acceleration, while the system's input are the instantaneous spike rates. The system's state dynamics is defined as a combination of a linear mapping from the previous estimated state and a kernel-based mapping tailored for modeling neural activities. In contrast to generative models, the activity-to-state mapping is learned using discriminative methods by minimizing a noise-robust loss function. We use this approach to predict hand trajectories on the basis of neural activity in motor cortex of behaving monkeys and find that the proposed approach is more accurate than both a static approach based on support vector regression and the Kalman filter.

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Spike sorting: Bayesian clustering of non-stationary data 04

Spike sorting: Bayesian clustering of non-stationary data

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

作者： Aharon Bar-Hillel Adam Spiro Eran Stark Neural Computation Center The Hebrew University of Jerusalem School of Computer Science and Engineering The Hebrew University of Jerusalem Department of Physiology The Hebrew University of Jerusalem

Spike sorting involves clustering spike trains recorded by a micro-electrode according to the source neuron. It is a complicated problem, which requires a lot of human labor, partly due to the non-stationary nature of the data. We propose an automated technique for the clustering of non-stationary Gaussian sources in a Bayesian framework. At a first search stage, data is divided into short time frames and candidate descriptions of the data as a mixture of Gaussians are computed for each frame. At a second stage transition probabilities between candidate mixtures are computed, and a globally optimal clustering is found as the MAP solution of the resulting probabilistic model. Transition probabilities are computed using local stationarity assumptions and are based on a Gaussian version of the Jensen-Shannon divergence. The method was applied to several recordings. The performance appeared almost indistinguishable from humans in a wide range of scenarios, including movement, merges, and splits of clusters.

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Euclidean embedding of co-occurrence data 04

Euclidean embedding of co-occurrence data

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Proceedings of the 18th International Conference on neural Information Processing Systems

作者： Amir Globerson Gal Chechik Fernando Pereira Naftali Tishby School of computer Science and Engineering Interdisciplinary Center for Neural Computation The Hebrew University Jerusalem Israel Computer Science Department Stanford University Stanford CA Department of Computer and Information Science University of Pennsylvania Philadelphia PA

Embedding algorithms search for low dimensional structure in complex data, but most algorithms only handle objects of a single type for which pairwise distances are specified. This paper describes a method for embedding objects of different types, such as images and text, into a single common Euclidean space based on their co-occurrence statistics. The joint distributions are modeled as exponentials of Euclidean distances in the low-dimensional embedding space, which links the problem to convex optimization over positive semidefinite matrices. The local structure of our embedding corresponds to the statistical correlations via random walks in the Euclidean space. We quantify the performance of our method on two text datasets, and show that it consistently and significantly outperforms standard methods of statistical correspondence modeling, such as multidimensional scaling and correspondence analysis.

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Multi-knockout genetic network analysis: the Rad6 example

Multi-knockout genetic network analysis: the Rad6 example

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Proceedings of the IEEE computational Systems Bioinformatics Conference, CSB

作者： A. Kaufman M. Kupiec E. Ruppin Center of Neural Computation Hebrew University Jerusalem Israel School of Computer Science and School of Medicine Tel-Aviv University Tel-Aviv Israel

A novel and rigorous multi-perturbation shapley value analysis (MSA) method has been recently presented. The method addresses the challenge of defining and calculating the functional causal contributions of elements of a biological system. This work presents the first study applying MSA to the analysis of gene knockout data. The MSA identifies the importance of genes in the Rad6 DNA repair pathway of the yeast S. cerevisiae, quantifying their contributions and characterizing their functional interactions. Incorporating additional biological knowledge, a new functional description of the Rad6 pathway is provided, predicting the existence of additional DNA polymerase and RFC-like complexes. The MSA is the first method for rigorously analyzing multi-knockout experiments, which are likely to soon become a standard and necessary tool for analyzing complex biological systems.

关键词： Genetics DNA Biological systems System identification Fungi Bioinformatics Organisms Biology computing Biotechnology computer science

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Margin analysis of the LVQ algorithm 15

Margin analysis of the LVQ algorithm

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16th Annual neural Information Processing Systems Conference, NIPS 2002

作者： Crammer, Koby Gilad-Bachrach, Ran Navot, Amir Tishby, Naftali School of Computer Science and Engineering Interdisciplinary Center for Neural Computation Hebrew University Jerusalem Israel

ISBN: (纸本)0262025507

Prototypes based algorithms are commonly used to reduce the computational complexity of Nearest-Neighbour (NN) classifiers. In this paper we discuss theoretical and algorithmical aspects of such algorithms. On the theory side, we present margin based generalization bounds that suggest that these kinds of classifiers can be more accurate then the 1-NN rule. Furthermore, we derived a training algorithm that selects a good set of prototypes using large margin principles. We also show that the 20 years old Learning Vector Quantization (LVQ) algorithm emerges naturally from our framework.

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Extracting relevant structures with side information 15

Extracting relevant structures with side information

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16th Annual neural Information Processing Systems Conference, NIPS 2002

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

ISBN: (纸本)0262025507

The problem of extracting the relevant aspects of data, in face of multiple conflicting structures, is inherent to modeling of complex data. Extracting structure in one random variable that is relevant for another variable has been principally addressed recently via the information bottleneck method [15]. However, such auxiliary variables often contain more information than is actually required due to structures that are irrelevant for the task. In many other cases it is in fact easier to specify what is irrelevant than what is, for the task at hand. Identifying the relevant structures, however, can thus be considerably improved by also minimizing the information about another, irrelevant, variable. In this paper we give a general formulation of this problem and derive its formal, as well as algorithmic, solution. Its operation is demonstrated in a synthetic example and in two real world problems in the context of text categorization and face images. While the original information bottleneck problem is related to rate distortion theory, with the distortion measure replaced by the relevant information, extracting relevant features while removing irrelevant ones is related to rate distortion with side information.

关键词： Signal distortion

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Natural images, Gaussian mixtures and dead leaves

Natural images, Gaussian mixtures and dead leaves

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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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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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