Browsing Computer Science and Automation (CSA) by Title
Now showing items 347-366 of 394
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Sparse Multiclass And Multi-Label Classifier Design For Faster Inference
(2013-06-20)Many real-world problems like hand-written digit recognition or semantic scene classification are treated as multiclass or multi-label classification prob-lems. Solutions to these problems using support vector machines (SVMs) ... -
Specification Synthesis with Constrained Horn Clauses
Many practical problems in software development, verification and testing rely on specifications. The problem of specification synthesis is to automatically find relational constraints for undefined functions, called ... -
Spill Code Minimization And Buffer And Code Size Aware Instruction Scheduling Techniques
(2009-05-19)Instruction scheduling and Software pipelining are important compilation techniques which reorder instructions in a program to exploit instruction level parallelism. They are essential for enhancing instruction level ... -
A Static Slicing Tool for Sequential Java Programs
(2018-07-28)A program slice consists of a subset of the statements of a program that can potentially affect values computed at some point of interest. Such a point of interest along with a set of variables is called a slicing criterion. ... -
Statistical Network Analysis: Community Structure, Fairness Constraints, and Emergent Behavior
Networks or graphs provide mathematical tools for describing and analyzing relational data. They are used in biology to model interactions between proteins, in economics to identify trade alliances among countries, in ... -
Stochastic Approximation Algorithms with Set-valued Dynamics : Theory and Applications
(2018-07-05)Stochastic approximation algorithms encompass a class of iterative schemes that converge to a sought value through a series of successive approximations. Such algorithms converge even when the observations are erroneous. ... -
Stochastic Approximation with Markov Noise: Analysis and applications in reinforcement learning
Stochastic approximation algorithms are sequential non-parametric methods for finding a zero or minimum of a function in the situation where only the noisy observations of the function values are available. Two time-scale ... -
Stochastic approximation with set-valued maps and Markov noise: Theoretical foundations and applications
Stochastic approximation algorithms produce estimates of a desired solution using noisy real world data. Introduced by Robbins and Monro, in 1951, stochastic approximation techniques have been instrumental in the asymptotic ... -
Stochastic Newton Methods With Enhanced Hessian Estimation
(2018-05-22)Optimization problems involving uncertainties are common in a variety of engineering disciplines such as transportation systems, manufacturing, communication networks, healthcare and finance. The large number of input ... -
Stochastic Optimization And Its Application In Reinforcement Learning
Numerous engineering fields, such as transportation systems, manufacturing, communication networks, healthcare, and finance, frequently encounter problems requiring optimization in the presence of uncertainty. Simulation-based ... -
Structured Regularization Through Convex Relaxations Of Discrete Penalties
Motivation. Empirical risk minimization(ERM) is a popular framework for learning predictive models from data, which has been used in various domains such as computer vision, text processing, bioinformatics, neuro-biology, ... -
Studies In Automatic Management Of Storage Systems
(2015-11-16)Autonomic management is important in storage systems and the space of autonomics in storage systems is vast. Such autonomic management systems can employ a variety of techniques depending upon the specific problem. In this ... -
A Study Of Quantum And Reversible Computing
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A Study of the Performance Benefits of Controlling Parallel Asynochrous Iteractive Applications
(Indian Institute of Science, 2005-03-11)High performance networks of workstation are becoming increasingly popular a parallel computing platform because of their lower cost. Both message passing and software distributed shared memory (DSM) programming paradigms ... -
A Study of Thompson Sampling Approach for the Sleeping Multi-Armed Bandit Problem
(2018-05-29)The multi-armed bandit (MAB) problem provides a convenient abstraction for many online decision problems arising in modern applications including Internet display advertising, crowdsourcing, online procurement, smart grids, ... -
Superscalar Processor Models Using Statistical Learning
(2009-06-24)Processor architectures are becoming increasingly complex and hence architects have to evaluate a large design space consisting of several parameters, each with a number of potential settings. In order to assist in guiding ... -
Supervised Classification of Missense Mutations as Pathogenic or Tolerated using Ensemble Learning Methods
(2018-07-09)Missense mutations account for more than 50% of the mutations known to be involved in human inherited diseases. Missense classification is a challenging task that involves sequencing of the genome, identifying the variations, ... -
Symmetry in Scalar Fields
(2018-01-09)Scalar fields are used to represent physical quantities measured over a domain of interest. Study of symmetric or repeating patterns in scalar fields is important in scientific data analysis because it gives deep insights ...