PURR Registry

Subject

Creator

Walter Gautschi, 0000-0001-9184-8899 287
Mireille Boutin, 0000-0002-0837-6577 3
Bartlomiej P. Rajwa 2
Courtney Falk 2
Jonas Hepp 2
Murat Dundar 2
Yellamraju Tarun 2
Adnan Firoze, 0000-0002-2751-009X 1
Akshaj Uppala 1
Austin J. Klasa 1

Type

Dataset 302
Series 1

Date

2023 1
2021 4
2020 55
2018 2
2017 124
2016 71
2015 7
2014 37
2012 2

subject: Computer Science

Total is 303 Results

Simplicity of K-means versus deepness of Deep Learning. A Case of Unsupervised Feature Learning with Limited Data

10.4231/R7N58J9Z

Baichuan Zhang , Bartlomiej P. Rajwa , Murat Dundar , Qiang Kou , Yicheng He

10/01/2015

A study contrasting K-means-based unsupervised feature learning and deep learning techniques for small data sets with limited intra- as well as inter-class diversity

bacterial colonies BARDOT Biomedical Engineering Computer Science deep learning Elastic light scattering Interdisciplinary Research K-Means Clustering Life Sciences Machine Learning representation learning

Daedalus

10.4231/R7736NV6

Austin Klasa , Courtney Falk

07/30/2015

Public cloud risk assessment tool.

Computer Science Information Security Java Modeling Public cloud providers Risk assessment software Source Code

Daedalus

10.4231/R7SX6B5R

Austin J. Klasa , Courtney Falk

08/19/2015

Public cloud risk assessment tool.

Computer Science Information Security Java Modeling Public cloud providers Risk assessment software Source Code

HUBbub 2013 Survey Data

10.4231/R7JD4TQM

Michael G. Zentner

08/31/2015

A CSV and accompanying HTML zip file of data taken at a survey about data sharing during HUBbub 2013.

Computer Science Survey

Source code for the paper Polynomial Fitting Known Plaintext Attack

10.4231/R7GH9FWM

James Alan Vaught

11/20/2015

This file contains C++ source code for the numerical experiments described in the paper Polynomial Fitting Known Plaintext Attack.

Computer Science cryptanalysis encryption Information Security known plaintext attack polynomial fitting

OPCBSPL: Orthogonal polynomials relative to cardinal B-spline weight functions

10.4231/R7NG4NKC

Walter Gautschi

10/28/2016

A stable and efficient discretization procedure is developed to compute recurrence coefficients for orthogonal polynomials whose weight function is a polynomial cardinal B-spline of order greater than, or equal to, one.

B-spline Computer Science Mathematics Modification algorithms for orthogonal polynomials Orthogonal polynomials polynomials Walter Gautschi Archives weight functions

Code and Dataset for Pattern Recognition Benchmarks

10.4231/R7610X8M

Jonas Hepp , Mireille Boutin , Yellamraju Tarun

02/03/2016

This code computed a sequence of bounds for the error rate of a pattern recognition method. The bounds correspond to the error rate that one would expect to achieve by simply selecting features at random and thresholding the feature (TARP) approach.

classification Computer Science Electrical and Computer Engineering feature evaluation image processing image recognition Pattern Recognition Pedestrian Classification

32-digit values of the first 100 recurrence coefficients for an Airy weight function

10.4231/R7V122R6

Walter Gautschi

10/19/2016

32-digit values of the first 100 recurrence coefficients for the (normalized) weight function w(x)=c*x^(-5/6)e^(-x)Ai((3x/2)^(2/3)) on [0,Inf], c=2^(-1/6)*3^(1/6)/pi^(1/2), where Ai is the Airy function

Airy weight function Computer Science Mathematics Modification algorithms for orthogonal polynomials Orthogonal polynomials Quadrature and cubature formulas Walter Gautschi Archives