Transparency and reproducibility in artificial intelligence
Прозрачность и воспроизводимость в области искусственного интеллекта
2020-10-14
SCID: 54.1/7d5hahab
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artificial intelligencedeep learningmachine learningreproducibilitytransparency
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Abstract (AI)
Open Access articles citing this article. Deep learning in the stock market—a systematic survey of practice, backtesting, and applications Kenniy Olorunnimbe & Herna Viktor Artificial Intelligence Review Open Access 30 June 2022 Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening Jenny Yang , Andrew A. S. Soltan & David A. Clifton npj Digital Medicine Open Access 07 June 2022 Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases Chirag Gupta , Pramod Chandrashekar … Daifeng Wang Journal of Neurodevelopmental Disorders Open Access 02 May 2022
Key Findings
1
The cited works address deep learning in stock markets, cross-site healthcare generalizability, and machine learning research on intellectual and developmental disabilities.
2
The provided text contains only the paper title and a list of citing articles, not the paper’s abstract or substantive findings.
3
The title identifies transparency and reproducibility in artificial intelligence as the paper’s central topic, but specific claims cannot be extracted.
Research Object
Transparency and reproducibility in artificial intelligence research
Research Subject
Transparency and reproducibility practices and requirements for artificial intelligence research
Publication Details
Publication Date
2020-10-14
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