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1	Material Type: Article	Exploiting data diversity in multi-domain federated learning Machine learning: science and technology, 2024-06, Vol.5 (2), p.025041 [Peer Reviewed Journal] 2024 The Author(s). Published by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;EISSN: 2632-2153 ;DOI: 10.1088/2632-2153/ad4768 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
2	Material Type: Article	Towards a machine-learned Poisson solver for low-temperature plasma simulations in complex geometries Machine learning: science and technology, 2024-06, Vol.5 (2), p.025031 [Peer Reviewed Journal] 2024 The Author(s). Published by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;EISSN: 2632-2153 ;DOI: 10.1088/2632-2153/ad4230 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
3	Material Type: Article	The impact of memory on learning sequence-to-sequence tasks Machine learning: science and technology, 2024-03, Vol.5 (1), p.015053 [Peer Reviewed Journal] 2024 The Author(s). Published by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;EISSN: 2632-2153 ;DOI: 10.1088/2632-2153/ad2feb Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
4	Material Type: Article	Ten years of generative adversarial nets (GANs): a survey of the state-of-the-art Machine learning: science and technology, 2024-03, Vol.5 (1), p.11001 [Peer Reviewed Journal] 2024 The Author(s). Published by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2632-2153 ;EISSN: 2632-2153 ;DOI: 10.1088/2632-2153/ad1f77 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
5	Material Type: Article	Synthetic pre-training for neural-network interatomic potentials Machine learning: science and technology, 2024-03, Vol.5 (1), p.15003 [Peer Reviewed Journal] 2024 The Author(s). Published by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2632-2153 ;EISSN: 2632-2153 ;DOI: 10.1088/2632-2153/ad1626 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
6	Material Type: Article	Phase transitions in the mini-batch size for sparse and dense two-layer neural networks Machine learning: science and technology, 2024-03, Vol.5 (1), p.15015 [Peer Reviewed Journal] 2024 The Author(s). Published by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2632-2153 ;EISSN: 2632-2153 ;DOI: 10.1088/2632-2153/ad1de6 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
7	Material Type: Article	Neural network field theories: non-Gaussianity, actions, and locality Machine learning: science and technology, 2024-03, Vol.5 (1), p.15002 [Peer Reviewed Journal] 2024 The Author(s). Published by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2632-2153 ;EISSN: 2632-2153 ;DOI: 10.1088/2632-2153/ad17d3 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
8	Material Type: Article	Observing Schrödinger’s cat with artificial intelligence: emergent classicality from information bottleneck Machine learning: science and technology, 2024-03, Vol.5 (1), p.015051 [Peer Reviewed Journal] 2024 The Author(s). Published by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;EISSN: 2632-2153 ;DOI: 10.1088/2632-2153/ad3330 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
9	Material Type: Article	WaveFormer: transformer-based denoising method for gravitational-wave data Machine learning: science and technology, 2024-03, Vol.5 (1), p.015046 [Peer Reviewed Journal] 2024 The Author(s). Published by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;EISSN: 2632-2153 ;DOI: 10.1088/2632-2153/ad2f54 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
10	Material Type: Article	Ergodicity, lack thereof, and the performance of reservoir computing with memristive networks Nano express, 2024-03, Vol.5 (1), p.015021 [Peer Reviewed Journal] 2024 The Author(s). Published by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2632-959X ;EISSN: 2632-959X ;DOI: 10.1088/2632-959X/ad2999 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
11	Material Type: Article	ATSFCNN: A Novel Attention-based Triple-Stream Fused CNN Model for Hyperspectral Image Classification Machine learning: science and technology, 2024-03, Vol.5 (1), p.015024 [Peer Reviewed Journal] 2024 The Author(s). Published by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2632-2153 ;EISSN: 2632-2153 ;DOI: 10.1088/2632-2153/ad1d05 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
12	Material Type: Article	Laziness, barren plateau, and noises in machine learning Machine learning: science and technology, 2024-03, Vol.5 (1), p.015058 [Peer Reviewed Journal] 2024 The Author(s). Published by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;EISSN: 2632-2153 ;DOI: 10.1088/2632-2153/ad35a3 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
13	Material Type: Article	Generative adversarial networks for data-scarce radiative heat transfer applications Machine learning: science and technology, 2024-03, Vol.5 (1), p.015060 [Peer Reviewed Journal] 2024 The Author(s). Published by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;EISSN: 2632-2153 ;DOI: 10.1088/2632-2153/ad33e1 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
14	Material Type: Article	CoRe optimizer: an all-in-one solution for machine learning Machine learning: science and technology, 2024-03, Vol.5 (1), p.15018 [Peer Reviewed Journal] 2024 The Author(s). Published by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2632-2153 ;EISSN: 2632-2153 ;DOI: 10.1088/2632-2153/ad1f76 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
15	Material Type: Article	Physics informed token transformer for solving partial differential equations Machine learning: science and technology, 2024-03, Vol.5 (1), p.015032 [Peer Reviewed Journal] 2024 The Author(s). Published by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;EISSN: 2632-2153 ;DOI: 10.1088/2632-2153/ad27e3 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
16	Material Type: Conference Proceeding	Bibliometric study with statistical patterns of industry 4.0 applied to process control Journal of physics. Conference series, 2024, Vol.2726 (1), p.012008 [Peer Reviewed Journal] Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/2726/1/012008 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
17	Material Type: Conference Proceeding	NAVMAT: an AI-powered pathway to knowledge sharing on material failures Journal of physics. Conference series, 2024, Vol.2692 (1), p.012036 [Peer Reviewed Journal] Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/2692/1/012036 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
18	Material Type: Article	Metal doped polyaniline as neuromorphic circuit elements for in-materia computing Science and technology of advanced materials, 2023-12, Vol.24 (1), p.2178815-2178815 [Peer Reviewed Journal] 2023 The Author(s). Published by National Institute for Materials Science in partnership with Taylor & Francis Group. 2023 ;2023 The Author(s). Published by National Institute for Materials Science in partnership with Taylor & Francis Group. ;2023 The Author(s). Published by National Institute for Materials Science in partnership with Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2023 The Author(s). Published by National Institute for Materials Science in partnership with Taylor & Francis Group. 2023 The Author(s) ;ISSN: 1468-6996 ;EISSN: 1878-5514 ;DOI: 10.1080/14686996.2023.2178815 ;PMID: 36872943 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
19	Material Type: Article	Looking at the posterior: accuracy and uncertainty of neural-network predictions Machine learning: science and technology, 2023-12, Vol.4 (4), p.45032 [Peer Reviewed Journal] 2023 The Author(s). Published by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2632-2153 ;EISSN: 2632-2153 ;DOI: 10.1088/2632-2153/ad0ab4 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
20	Material Type: Article	Bayesian renormalization Machine learning: science and technology, 2023-12, Vol.4 (4), p.45011 [Peer Reviewed Journal] 2023 The Author(s). Published by IOP Publishing Ltd ;2023 The Author(s). Published by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2632-2153 ;EISSN: 2632-2153 ;DOI: 10.1088/2632-2153/ad0102 ;CODEN: MLSTCK Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by