Alexander Genkin

Alexander Genkin
NYU Langone Medical Center | NYUMC · Department of Physiology and Neuroscience

Ph. D. in Computer Science

About

26
Publications
7,201
Reads
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1,098
Citations
Citations since 2016
7 Research Items
439 Citations
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Introduction
Alexander Genkin currently works at the Department of Physiology and Neuroscience, NYU Langone Medical Center. Alexander does research in Artificial Intelligence, Data Mining and Artificial Neural Network.

Publications

Publications (26)
Preprint
A major problem in motor control is understanding how the brain plans and executes proper movements in the face of delayed and noisy stimuli. A prominent framework for addressing such control problems is Optimal Feedback Control (OFC). OFC generates control actions that optimize behaviorally relevant criteria by integrating noisy sensory stimuli an...
Chapter
Full-text available
Semi-supervised learning algorithms typically construct a weighted graph of data points to represent a manifold. However, an explicit graph representation is problematic for neural networks operating in the online setting. Here, we propose a feed-forward neural network capable of semi-supervised learning on manifolds without using an explicit graph...
Preprint
Full-text available
Semi-supervised learning algorithms typically construct a weighted graph of data points to represent a manifold. However, an explicit graph representation is problematic for neural networks operating in the online setting. Here, we propose a feed-forward neural network capable of semi-supervised learning on manifolds without using an explicit graph...
Preprint
Full-text available
Many neurons in the brain, such as place cells in the rodent hippocampus, have localized receptive fields, i.e., they respond to a small neighborhood of stimulus space. What is the functional significance of such representations and how can they arise? Here, we propose that localized receptive fields emerge in similarity-preserving networks of rect...
Preprint
Full-text available
A key step in insect olfaction is the transformation of a dense representation of odors in a small population of neurons - projection neurons (PNs) of the antennal lobe - into a sparse representation in a much larger population of neurons -Kenyon cells (KCs) of the mushroom body. What computational purpose does this transformation serve? We propose...
Conference Paper
Full-text available
A key step in insect olfaction is the transformation of a dense representation of odors in a small population of neurons-projection neurons (PNs) of the antennal lobe-into a sparse representation in a much larger population of neurons-Kenyon cells (KCs) of the mushroom body. What computational purpose does this transformation serve? We propose that...
Article
Full-text available
Generalized linear model with $L_1$ and $L_2$ regularization is a widely used technique for solving classification, class probability estimation and regression problems. With the numbers of both features and examples growing rapidly in the fields like text mining and clickstream data analysis parallelization and the use of cluster architectures bec...
Conference Paper
Full-text available
Logistic regression is a widely used technique for solving classification and class probability estimation problems in text mining, biometrics and clickstream data analysis. Solving logistic regression with L1-regularization in distributed settings is an important problem. This problem arises when training dataset is very large and cannot fit the m...
Conference Paper
Full-text available
A neuron is a basic physiological and computational unit of the brain. While much is known about the physiological properties of a neuron, its computational role is poorly understood. Here we propose to view a neuron as a signal processing device that represents the incoming streaming data matrix as a sparse vector of synaptic weights scaled by an...
Article
Full-text available
Computing sparse redundant representations is an important problem in both applied mathematics and neuroscience. In many applications, this problem must be solved in an energy-efficient way. Here, we propose a hybrid distributed algorithm (HDA), which solves this problem on a network of simple nodes communicating by low-bandwidth channels. HDA node...
Conference Paper
Full-text available
Future progress in neuroscience hinges on reconstruc- tion of neuronal circuits to the level of individual synapses. Because of the specifics of neuronal architecture, imaging must be done with very high resolution and throughput. While Electron Microscopy (EM) achieves the required res- olution in the transverse directions, its depth resolution is...
Article
Full-text available
Logistic regression analysis of high-dimensional data, such as natural language text, poses computational and statistical challenges. Maximum likelihood estimation often fails in these applications. We present a simple Bayesian logistic regression approach that uses a Laplace prior to avoid overfitting and produces sparse predictive models for text...
Conference Paper
Full-text available
Supervised learning approaches to text classification are in practice often required to work with small and unsystematically collected training sets. The alternative to supervised learning is usually viewed to be building classifiers by hand, using a domain expert's understanding of which features of the text are related to the class of interest. T...
Article
Full-text available
Motivated by high-dimensional applications in authorship attribution, we describe a Bayesian multinomial logistic regression model together with an associated learning algorithm.
Conference Paper
Full-text available
This report describes DIMACS work on the text categoriza- tion task of the TREC 2005 Genomics track. Our approach to this task was similar to the triage subtask studied in the TREC 2004 Genomics track. We applied Bayesian logistic regression and achieved good eectiveness on all categories.
Article
Full-text available
This paper studies regularized logistic regression and its ap-plication to text categorization. In particular we examine a Bayesian approach, lasso logistic regression, that simul-taneously selects variables and provides regularization. We present an efficient training algorithm for this approach, and show that the resulting classifiers are both co...
Article
Full-text available
on two of the groups of entity resolution problems, ER1 and ER2 for the KDD Challenge in 2005. We presume that the situation is intended to mimic, using abstracts and author information from the life sciences, some real world problem, in which it is important to recognize the identity of an individual, even though he may share that name with other...
Conference Paper
Full-text available
DIMACS participated in the text categorization and ad hoc retrieval tasks of the TREC 2004 Genomics track. For the categorization task, we tackled the triage and annotation hierarchy subtasks. and biology of the laboratory mouse. In particular, the Mouse Genome Database (MGD) contains information on the characteristics and functions of genes in the...
Article
This paper describes an application of Bayesian logistic regression to text cate- gorization. In particular we examine so-called "sparse" Bayesian models that simu- taneously select variables and provide shrinkage. We present empirical evidence that these models retain good predictive capabilities while oering significant computational advantages.
Article
U) This paper empirically compares the performance of di#erent Bayesian models for text categorization. In particular we examine so-called "sparse" Bayesian models that explicitly favor simplicity. We present empirical evidence that these models retain good predictive capabilities while o#ering significant computational advantages.
Article
Full-text available
This paper empirically compares the performance of di#erent Bayesian models for text categorization. In particular we examine so-called "sparse" Bayesian models that explicitly favor simplicity. We present empirical evidence that these models retain good predictive capabilities while o#ering significant computational advantages.
Article
We show how several problems in different areas of data mining and knowledge discovery can be viewed as finding the optimal covering of a finite set. Many such problems arise in biomedical and bioinformatics research. For example, protein functional annotation based on sequence information is an ubiquitous bioinformatics problem. It consists of fin...
Article
Full-text available
Assume that a dissimilarity measure between elements and subsets of the set being clustered is given. We define the transformation of the set of subsets under which each subset is transformed into the set of all elements whose dissimilarity to it is not greater than a given threshold. Then a cluster is defined as a fixed point of this transformatio...
Article
Full-text available
Individuals have distinctive ways of speaking and writing, and there exists a long history of linguistic and stylistic investigation into authorship attribution. In recent years, practical applications for authorship attribution have grown in areas such as intelligence (linking intercepted messages to each other and to known terrorists), criminal l...

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