4,213 research outputs found
NIPS - Not Even Wrong? A Systematic Review of Empirically Complete Demonstrations of Algorithmic Effectiveness in the Machine Learning and Artificial Intelligence Literature
Objective: To determine the completeness of argumentative steps necessary to
conclude effectiveness of an algorithm in a sample of current ML/AI supervised
learning literature.
Data Sources: Papers published in the Neural Information Processing Systems
(NeurIPS, n\'ee NIPS) journal where the official record showed a 2017 year of
publication.
Eligibility Criteria: Studies reporting a (semi-)supervised model, or
pre-processing fused with (semi-)supervised models for tabular data.
Study Appraisal: Three reviewers applied the assessment criteria to determine
argumentative completeness. The criteria were split into three groups,
including: experiments (e.g real and/or synthetic data), baselines (e.g
uninformed and/or state-of-art) and quantitative comparison (e.g. performance
quantifiers with confidence intervals and formal comparison of the algorithm
against baselines).
Results: Of the 121 eligible manuscripts (from the sample of 679 abstracts),
99\% used real-world data and 29\% used synthetic data. 91\% of manuscripts did
not report an uninformed baseline and 55\% reported a state-of-art baseline.
32\% reported confidence intervals for performance but none provided references
or exposition for how these were calculated. 3\% reported formal comparisons.
Limitations: The use of one journal as the primary information source may not
be representative of all ML/AI literature. However, the NeurIPS conference is
recognised to be amongst the top tier concerning ML/AI studies, so it is
reasonable to consider its corpus to be representative of high-quality
research.
Conclusion: Using the 2017 sample of the NeurIPS supervised learning corpus
as an indicator for the quality and trustworthiness of current ML/AI research,
it appears that complete argumentative chains in demonstrations of algorithmic
effectiveness are rare
Satellite Constellation Pattern Optimization for Complex Regional Coverage
The use of regional coverage satellite constellations is on the rise, urging
the need for an optimal constellation design method for complex regional
coverage. Traditional constellations are often designed for continuous global
coverage, and the few existing regional constellation design methods lead to
suboptimal solutions for periodically time-varying or spatially-varying
regional coverage requirements. This paper introduces a new general approach to
design an optimal constellation pattern that satisfies such complex regional
coverage requirements. To this end, the circular convolution nature of the
repeating ground track orbit and common ground track constellation is
formalized. This formulation enables a scalable constellation pattern analysis
for multiple target areas and with multiple sub-constellations. The formalized
circular convolution relationship is first used to derive a baseline
constellation pattern design method with the conventional assumption of
symmetry. Next, a novel method based on binary integer linear programming is
developed, which aims to optimally design a constellation pattern with the
minimum number of satellites. This binary integer linear programming method is
shown to achieve optimal constellation patterns for general problem settings
that the baseline method cannot achieve. Five illustrative examples are
analyzed to demonstrate the value of the proposed new approach.Comment: 47 pages, 23 figures, Journal of Spacecraft and Rockets (Published
- …