{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T10:48:03Z","timestamp":1778150883824,"version":"3.51.4"},"reference-count":79,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T00:00:00Z","timestamp":1740960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2025,3,3]]},"abstract":"<jats:p>Brain-computer interfaces are groundbreaking technology whereby brain signals are used to control external devices. Despite some advances in recent years, electroencephalogram (EEG)-based motor-imagery tasks face challenges, such as amplitude and phase variability and complex spatial correlations, with a need for smaller models and faster inference. In this study, we develop a prototype, called the Lightweight Geometric Learning Brain-Computer Interface (LGL-BCI), which uses our customized geometric deep learning architecture for swift model inference without sacrificing accuracy. LGL-BCI contains an EEG channel selection module via a feature decomposition algorithm to reduce the dimensionality of a symmetric positive definite matrix, providing adaptiveness among the continuously changing EEG signal. Meanwhile, a built-in lossless transformation helps boost the inference speed. The performance of our solution was evaluated using two real-world EEG devices and two public EEG datasets. LGL-BCI demonstrated significant improvements, achieving an accuracy of 82.54% compared to 62.22% for the state-of-the-art approach. Furthermore, LGL-BCI uses fewer parameters (64.9Kvs. 183.7K), highlighting its computational efficiency. These findings underscore both the superior accuracy and computational efficiency of LGL-BCI, demonstrating the feasibility and robustness of geometric deep learning in motor-imagery brain-computer interface applications.<\/jats:p>","DOI":"10.1145\/3699732","type":"journal-article","created":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T12:10:14Z","timestamp":1741090214000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["LGL-BCI: A Motor-Imagery-Based Brain-Computer Interface with Geometric Learning"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0788-1448","authenticated-orcid":false,"given":"Jianchao","family":"Lu","sequence":"first","affiliation":[{"name":"Macquarie University, School of Computing, Macquarie Park, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5742-7414","authenticated-orcid":false,"given":"Yuzhe","family":"Tian","sequence":"additional","affiliation":[{"name":"Macquarie University, School of Computing, Macquarie Park, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6821-2710","authenticated-orcid":false,"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Macquarie University, School of Computing, Macquarie Park, NSW, Australia, and University of North Texas, Department of Information Science, Denton, Texas, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3326-4147","authenticated-orcid":false,"given":"Quan Z.","family":"Sheng","sequence":"additional","affiliation":[{"name":"Macquarie University, School of Computing, Macquarie Park, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2572-2355","authenticated-orcid":false,"given":"Xi","family":"Zheng","sequence":"additional","affiliation":[{"name":"Macquarie University, School of Computing, Macquarie Park, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,3,4]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Mohammed Azmi Al-Betar, Mohammed A Awadallah, Karrar Hameed Abdulkareem, Mazin Abed Mohammed, Seifedine Kadry, V Rajinikanth, Seungmin Rho, et al.","author":"Alkareem Alyasseri Zaid Abdi","year":"2022","unstructured":"Zaid Abdi Alkareem Alyasseri, Osama Ahmad Alomari, Mohammed Azmi Al-Betar, Mohammed A Awadallah, Karrar Hameed Abdulkareem, Mazin Abed Mohammed, Seifedine Kadry, V Rajinikanth, Seungmin Rho, et al. 2022. EEG channel selection using multiobjective cuckoo search for person identification as protection system in healthcare applications. Computational Intelligence and Neuroscience 2022 (2022)."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/RADIOELEK.2019.8733482"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2019.00221"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2012.00039"},{"key":"e_1_2_1_5_1","volume-title":"2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). IEEE, 2390--2397","author":"Ang Kai Keng","year":"2008","unstructured":"Kai Keng Ang, Zheng Yang Chin, Haihong Zhang, and Cuntai Guan. 2008. Filter bank common spatial pattern (FBCSP) in brain-computer interface. In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). IEEE, 2390--2397."},{"key":"e_1_2_1_6_1","volume-title":"Laura Astolfi, Stefan Haufe, and Daniele Marinazzo.","author":"Anzolin Alessandra","year":"2018","unstructured":"Alessandra Anzolin, Paolo Presti, Frederik Van de Steen, Laura Astolfi, Stefan Haufe, and Daniele Marinazzo. 2018. Effect of head volume conduction on directed connectivity estimated between reconstructed EEG sources. bioRxiv (2018), 251223."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3048385"},{"key":"e_1_2_1_8_1","volume-title":"Multivariate analysis. Supplement to the journal of the royal statistical society 9, 2","author":"Bartlett Maurice S","year":"1947","unstructured":"Maurice S Bartlett. 1947. Multivariate analysis. Supplement to the journal of the royal statistical society 9, 2 (1947), 176--197."},{"key":"e_1_2_1_9_1","volume-title":"Gustavo Christofoletti, Luis Carlos Paschoarelli, and Fausto Orsi Medola.","author":"da Silva Bertolaccini Guilherme","year":"2018","unstructured":"Guilherme da Silva Bertolaccini, Idinei Francisco Pires de Carvalho Filho, Gustavo Christofoletti, Luis Carlos Paschoarelli, and Fausto Orsi Medola. 2018. The influence of axle position and the use of accessories on the activity of upper limb muscles during manual wheelchair propulsion. International journal of occupational safety and ergonomics 24, 2 (2018), 311--315."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmrj.2018.03.022"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2693418"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.230"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.3390\/bios12100772"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1080\/2326263X.2017.1297192"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2022.3154885"},{"key":"e_1_2_1_16_1","unstructured":"Ernie Croot. 2005. The Rayleigh principle for finding eigenvalues. Technical Report. Tech. rep. Online Georgia Institute of Technology. Accessed: Feb 2024."},{"key":"e_1_2_1_17_1","volume-title":"Scatter-based common spatial patterns-a unified spatial filtering framework. arXiv preprint arXiv:2303.06019","author":"Dong Jinlong","year":"2023","unstructured":"Jinlong Dong, Milana Komosar, Johannes Vorwerk, Daniel Baumgarten, and Jens Haueisen. 2023. Scatter-based common spatial patterns-a unified spatial filtering framework. arXiv preprint arXiv:2303.06019 (2023)."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.983602"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.15453\/2168-6408.1399"},{"key":"e_1_2_1_20_1","volume-title":"The lottery ticket hypothesis: Finding sparse, trainable neural networks. arXiv preprint arXiv:1803.03635","author":"Frankle Jonathan","year":"2018","unstructured":"Jonathan Frankle and Michael Carbin. 2018. The lottery ticket hypothesis: Finding sparse, trainable neural networks. arXiv preprint arXiv:1803.03635 (2018)."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1049\/ji-3-2.1946.0074"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.3390\/e23091117"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2655048"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10866"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11634-020-00426-3"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21196672"},{"key":"e_1_2_1_28_1","volume-title":"CNN-based Subject-Transfer Approach for Training Minimized Lower-Limb MI-BCIs. In 2022 10th International Winter Conference on Brain-Computer Interface (BCI). IEEE, 1--4.","author":"Jeong Ji-Hyeok","year":"2022","unstructured":"Ji-Hyeok Jeong, Keun-Tae Kim, Song Joo Lee, Dong-Joo Kim, and Hyungmin Kim. 2022. CNN-based Subject-Transfer Approach for Training Minimized Lower-Limb MI-BCIs. In 2022 10th International Winter Conference on Brain-Computer Interface (BCI). IEEE, 1--4."},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2023.3243992"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2020.00231"},{"key":"e_1_2_1_31_1","volume-title":"Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysis. arXiv preprint arXiv:2211.02641","author":"Ju Ce","year":"2022","unstructured":"Ce Ju and Cuntai Guan. 2022. Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysis. arXiv preprint arXiv:2211.02641 (2022)."},{"key":"e_1_2_1_32_1","volume-title":"Tensor-cspnet: A novel geometric deep learning framework for motor imagery classification","author":"Ju Ce","year":"2022","unstructured":"Ce Ju and Cuntai Guan. 2022. Tensor-cspnet: A novel geometric deep learning framework for motor imagery classification. IEEE Transactions on Neural Networks and Learning Systems (2022)."},{"key":"e_1_2_1_33_1","volume-title":"Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective From the Time-Frequency Analysis","author":"Ju Ce","year":"2023","unstructured":"Ce Ju and Cuntai Guan. 2023. Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective From the Time-Frequency Analysis. IEEE Transactions on Neural Networks and Learning Systems (2023)."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1080\/17483107.2022.2111723"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01129656"},{"key":"e_1_2_1_36_1","volume-title":"Methods towards invasive human brain computer interfaces. Advances in neural information processing systems 17","author":"Lal Thomas","year":"2004","unstructured":"Thomas Lal, Thilo Hinterberger, Guido Widman, Michael Schr\u00f6der, N Hill, Wolfgang Rosenstiel, Christian Elger, Niels Birbaumer, and Bernhard Sch\u00f6lkopf. 2004. Methods towards invasive human brain computer interfaces. Advances in neural information processing systems 17 (2004)."},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aace8c"},{"key":"e_1_2_1_38_1","volume-title":"Deep learning. Nature 521, 7553","author":"LeCun Yann","year":"2015","unstructured":"Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436--444."},{"key":"e_1_2_1_39_1","volume-title":"EEG dataset and OpenBMI toolbox for three BCI paradigms: An investigation into BCI illiteracy. GigaScience 8, 5","author":"Lee Min-Ho","year":"2019","unstructured":"Min-Ho Lee, O-Yeon Kwon, Yong-Jeong Kim, Hong-Kyung Kim, Young-Eun Lee, John Williamson, Siamac Fazli, and Seong-Whan Lee. 2019. EEG dataset and OpenBMI toolbox for three BCI paradigms: An investigation into BCI illiteracy. GigaScience 8, 5 (2019), giz002."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0085192"},{"key":"e_1_2_1_41_1","volume-title":"Temporal shift module for efficient video understanding. CoRR abs\/1811.08383","author":"Lin Ji","year":"2018","unstructured":"Ji Lin, Chuang Gan, and Song Han. 1811. Temporal shift module for efficient video understanding. CoRR abs\/1811.08383 (2018)."},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1137\/18M1221084"},{"key":"e_1_2_1_43_1","volume-title":"A Novel Convolutional Neural Network Architecture with a Continuous Symmetry. arXiv preprint arXiv:2308.01621","author":"Liu Yao","year":"2023","unstructured":"Yao Liu, Hang Shao, and Bing Bai. 2023. A Novel Convolutional Neural Network Architecture with a Continuous Symmetry. arXiv preprint arXiv:2308.01621 (2023)."},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aab2f2"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2010.2082539"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2010.2082540"},{"key":"e_1_2_1_47_1","volume-title":"PearNet: A Pearson Correlation-based Graph Attention Network for Sleep Stage Recognition. In 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 1--8.","author":"Lu Jianchao","year":"2022","unstructured":"Jianchao Lu, Yuzhe Tian, Shuang Wang, Michael Sheng, and Xi Zheng. 2022. PearNet: A Pearson Correlation-based Graph Attention Network for Sleep Stage Recognition. In 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 1--8."},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2023.3299355"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21155135"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2023.3243698"},{"key":"e_1_2_1_51_1","volume-title":"Neethu Robinson, A Prasad Vinod, Seong-Whan Lee, and Cuntai Guan.","author":"Mane Ravikiran","year":"2021","unstructured":"Ravikiran Mane, Effie Chew, Karen Chua, Kai Keng Ang, Neethu Robinson, A Prasad Vinod, Seong-Whan Lee, and Cuntai Guan. 2021. FBCNet: A multi-view convolutional neural network for brain-computer interface. arXiv preprint arXiv:2104.01233 (2021)."},{"key":"e_1_2_1_52_1","doi-asserted-by":"crossref","unstructured":"Sean L Metzger Kaylo T Littlejohn Alexander B Silva David A Moses Margaret P Seaton Ran Wang Maximilian E Dougherty Jessie R Liu Peter Wu Michael A Berger et al. 2023. A high-performance neuroprosthesis for speech decoding and avatar control. Nature 620 7976 (2023) 1037--1046.","DOI":"10.1038\/s41586-023-06443-4"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21186285"},{"key":"e_1_2_1_54_1","first-page":"31116","article-title":"MAtt: a manifold attention network for EEG decoding","volume":"35","author":"Pan Yue-Ting","year":"2022","unstructured":"Yue-Ting Pan, Jing-Lun Chou, and Chun-Shu Wei. 2022. MAtt: a manifold attention network for EEG decoding. Advances in Neural Information Processing Systems 35 (2022), 31116--31129.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-005-3222-z"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2005.12.003"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci14020160"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1101\/2022.03.17.481909"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2789927"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/4\/3\/012"},{"key":"e_1_2_1_61_1","first-page":"3","article-title":"Data with non-Euclidean geometry and its characterization","volume":"2","author":"Singh Prem Kumar","year":"2022","unstructured":"Prem Kumar Singh. 2022. Data with non-Euclidean geometry and its characterization. Journal of Artificial Intelligence and Technology 2, 1 (2022), 3--8.","journal-title":"Journal of Artificial Intelligence and Technology"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac0584"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac115d"},{"key":"e_1_2_1_64_1","doi-asserted-by":"crossref","unstructured":"Michael Tangermann Klaus-Robert M\u00fcller Ad Aertsen Niels Birbaumer Christoph Braun Clemens Brunner Robert Leeb Carsten Mehring Kai J Miller Gernot Mueller-Putz et al. 2012. Review of the BCI competition IV. Frontiers in Neuroscience (2012) 55.","DOI":"10.3389\/fnins.2012.00055"},{"key":"e_1_2_1_65_1","volume-title":"Human motion diffusion model. arXiv preprint arXiv:2209.14916","author":"Tevet Guy","year":"2022","unstructured":"Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, and Amit H Bermano. 2022. Human motion diffusion model. arXiv preprint arXiv:2209.14916 (2022)."},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2022.831528"},{"key":"e_1_2_1_67_1","volume-title":"Jorge Pardo, Bastien Orset, Kyuhwa Lee, Mirko Aach, Thomas Armin Schildhauer, Ram\u00f3n Mart\u00ednez-Olivera, and Jos\u00e9 del R Mill\u00e1n.","author":"Tonin Luca","year":"2022","unstructured":"Luca Tonin, Serafeim Perdikis, Taylan Deniz Kuzu, Jorge Pardo, Bastien Orset, Kyuhwa Lee, Mirko Aach, Thomas Armin Schildhauer, Ram\u00f3n Mart\u00ednez-Olivera, and Jos\u00e9 del R Mill\u00e1n. 2022. Learning to control a BMI-driven wheelchair for people with severe tetraplegia. Iscience 25, 12 (2022)."},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2014.08.229"},{"key":"e_1_2_1_69_1","volume-title":"Attention is all you need. Advances in Neural Information Processing Systems 30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017)."},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2023.3257319"},{"key":"e_1_2_1_71_1","volume-title":"MI-BMInet: An efficient convolutional neural network for motor imagery brain-Machine interfaces with EEG channel selection","author":"Wang Xiaying","year":"2024","unstructured":"Xiaying Wang, Michael Hersche, Michele Magno, and Luca Benini. 2024. MI-BMInet: An efficient convolutional neural network for motor imagery brain-Machine interfaces with EEG channel selection. IEEE Sensors Journal (2024)."},{"key":"e_1_2_1_72_1","unstructured":"Xu Wang Konstantinos Slavakis and Gilad Lerman. 2015. Multi-manifold modeling in non-Euclidean spaces. In Artificial Intelligence and Statistics. PMLR 1023--1032."},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2023.3236372"},{"key":"e_1_2_1_74_1","volume-title":"Feasibility of decoding visual information from EEG. Brain-Computer Interfaces","author":"Wilson Holly","year":"2023","unstructured":"Holly Wilson, Xi Chen, Mohammad Golbabaee, Michael J Proulx, and Eamonn O'Neill. 2023. Feasibility of decoding visual information from EEG. Brain-Computer Interfaces (2023), 1--28."},{"key":"e_1_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2016.2587939"},{"key":"e_1_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2016.2627016"},{"key":"e_1_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2014.2312397"},{"key":"e_1_2_1_78_1","volume-title":"Evaluation and Optimization of Gradient Compression for Distributed Deep Learning. arXiv preprint arXiv:2306.08881","author":"Zhang Lin","year":"2023","unstructured":"Lin Zhang, Longteng Zhang, Shaohuai Shi, Xiaowen Chu, and Bo Li. 2023. Evaluation and Optimization of Gradient Compression for Distributed Deep Learning. arXiv preprint arXiv:2306.08881 (2023)."},{"key":"e_1_2_1_79_1","volume-title":"Scalable MatMul-free Language Modeling. arXiv preprint arXiv:2406.02528","author":"Zhu Rui-Jie","year":"2024","unstructured":"Rui-Jie Zhu, Yu Zhang, Ethan Sifferman, Tyler Sheaves, Yiqiao Wang, Dustin Richmond, Peng Zhou, and Jason K Eshraghian. 2024. Scalable MatMul-free Language Modeling. arXiv preprint arXiv:2406.02528 (2024)."}],"container-title":["Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3699732","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3699732","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T19:31:33Z","timestamp":1755891093000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3699732"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,3]]},"references-count":79,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,3,3]]}},"alternative-id":["10.1145\/3699732"],"URL":"https:\/\/doi.org\/10.1145\/3699732","relation":{},"ISSN":["2474-9567"],"issn-type":[{"value":"2474-9567","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,3]]},"assertion":[{"value":"2025-03-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}