526,719 research outputs found
Learning from the past with experiment databases
Thousands of Machine Learning research papers contain experimental comparisons that usually have been conducted with a single focus of interest, and detailed results are usually lost after publication. Once past experiments are collected in experiment databases they allow for additional and possibly much broader investigation. In this paper, we show how to use such a repository to answer various interesting research questions about learning algorithms and to verify a number of recent studies. Alongside performing elaborate comparisons and rankings of algorithms, we also investigate the effects of algorithm parameters and data properties, and study the learning curves and bias-variance profiles of algorithms to gain deeper insights into their behavior
Towards realistic artificial benchmark for community detection algorithms evaluation
Assessing the partitioning performance of community detection algorithms is
one of the most important issues in complex network analysis. Artificially
generated networks are often used as benchmarks for this purpose. However,
previous studies showed their level of realism have a significant effect on the
algorithms performance. In this study, we adopt a thorough experimental
approach to tackle this problem and investigate this effect. To assess the
level of realism, we use consensual network topological properties. Based on
the LFR method, the most realistic generative method to date, we propose two
alternative random models to replace the Configuration Model originally used in
this algorithm, in order to increase its realism. Experimental results show
both modifications allow generating collections of community-structured
artificial networks whose topological properties are closer to those
encountered in real-world networks. Moreover, the results obtained with eleven
popular community identification algorithms on these benchmarks show their
performance decrease on more realistic networks
Parallel matrix inversion techniques
In this paper, we present techniques for inverting sparse, symmetric and positive definite matrices on parallel and distributed computers. We propose two algorithms, one for SIMD implementation and the other for MIMD implementation. These algorithms are modified versions of Gaussian elimination and they take into account the sparseness of the matrix. Our algorithms perform better than the general parallel Gaussian elimination algorithm. In order to demonstrate the usefulness of our technique, we implemented the snake problem using our sparse matrix algorithm. Our studies reveal that the proposed sparse matrix inversion algorithm significantly reduces the time taken for obtaining the solution of the snake problem. In this paper, we present the results of our experimental work
A Reproducible Study on Remote Heart Rate Measurement
This paper studies the problem of reproducible research in remote
photoplethysmography (rPPG). Most of the work published in this domain is
assessed on privately-owned databases, making it difficult to evaluate proposed
algorithms in a standard and principled manner. As a consequence, we present a
new, publicly available database containing a relatively large number of
subjects recorded under two different lighting conditions. Also, three
state-of-the-art rPPG algorithms from the literature were selected, implemented
and released as open source free software. After a thorough, unbiased
experimental evaluation in various settings, it is shown that none of the
selected algorithms is precise enough to be used in a real-world scenario
Towards the theory of hardness of materials
Recent studies showed that hardness, a complex property, can be calculated
using very simple approaches or even analytical formulae. These form the basis
for evaluating controversial experimental results (as we illustrate for
TiO2-cotunnite) and enable a systematic search for novel hard materials, for
instance, using global optimization algorithms (as we show on the example of
SiO2 polymorphs)
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