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Machine Learning: Science and Technology (MLST)

Publisher :

IOP Publishing

Scopus Profile
Peer reviewed only
Scopus Profile
Open Access
  • Science
  • Technology
  • Physics

    • +3

e-ISSN :

2632-2153

Issue Frequency :

Quarterly

Impact Factor :

6.3

Est. Year :

2020

Mobile :

4401179297481

DOI :

YES

Country :

Afghanistan

Language :

English

APC :

YES

Impact Factor Assignee :

Google Scholar

Email :

mlst@ioppublishing.org

Journal Descriptions

Machine Learning: Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Machine Learning: Science and Technology™ is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: i) advance the state of machine learning-driven applications in the sciences, or ii) make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.


Machine Learning: Science and Technology (MLST) is :

International, Peer-Reviewed, Open Access, Refereed, Science, Technology, Physics, Biology, Computer Science, Artificial Intelligence , Online Quarterly Journal

UGC Approved, ISSN Approved: P-ISSN , E-ISSN - 2632-2153, Established in - 2020, Impact Factor - 6.3

Provide Crossref DOI

Indexed in Scopus, WoS, DOAJ, PubMed

Not indexed in UGC CARE

Indexing

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Scopus
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WoS
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DOAJ

+9 More

Publications of MLST

Research Article
  • dott image Kai Yi
  • dott image December, 2024

ABCMB: Deep Delensing Assisted Likelihood-Free Inference from CMB Polarization Maps

The existence of a cosmic background of primordial gravitational waves (PGWB) is a robust prediction of inflationary cosmology, but it has so far evaded discovery. The most promising avenue ...

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