{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T07:57:31Z","timestamp":1786089451354,"version":"3.56.0"},"publisher-location":"New York, NY, USA","reference-count":32,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"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":[],"published-print":{"date-parts":[[2024,11,4]]},"DOI":"10.1145\/3678957.3685724","type":"proceedings-article","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T04:35:53Z","timestamp":1730262953000},"page":"382-386","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Low-Rank Adaptation of Time Series Foundational Models for Out-of-Domain Modality Forecasting"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4298-1399","authenticated-orcid":false,"given":"Divij","family":"Gupta","sequence":"first","affiliation":[{"name":"AI Engineering Team, SpassMed Inc., Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0035-9860","authenticated-orcid":false,"given":"Anubhav","family":"Bhatti","sequence":"additional","affiliation":[{"name":"AI Engineering Team, SpassMed Inc., Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5463-0563","authenticated-orcid":false,"given":"Suraj","family":"Parmar","sequence":"additional","affiliation":[{"name":"AI Engineering Team, SpassMed Inc., Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6719-8549","authenticated-orcid":false,"given":"Chen","family":"Dan","sequence":"additional","affiliation":[{"name":"AI Engineering Team, SpassMed Inc., Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1892-6525","authenticated-orcid":false,"given":"Yuwei","family":"Liu","sequence":"additional","affiliation":[{"name":"AI Engineering Team, SpassMed Inc., Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5293-1404","authenticated-orcid":false,"given":"Bingjie","family":"Shen","sequence":"additional","affiliation":[{"name":"AI Engineering Team, SpassMed Inc., Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6488-7988","authenticated-orcid":false,"given":"San","family":"Lee","sequence":"additional","affiliation":[{"name":"AI Engineering Team, SpassMed Inc., Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,11,4]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Chronos: Learning the language of time series. arXiv preprint arXiv:2403.07815","author":"Ansari Abdul\u00a0Fatir","year":"2024","unstructured":"Abdul\u00a0Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama\u00a0Sundar Rangapuram, Sebastian\u00a0Pineda Arango, Shubham Kapoor, 2024. Chronos: Learning the language of time series. arXiv preprint arXiv:2403.07815 (2024)."},{"key":"e_1_3_2_1_2_1","volume-title":"Foundational models defining a new era in vision: A survey and outlook. arXiv preprint arXiv:2307.13721","author":"Awais Muhammad","year":"2023","unstructured":"Muhammad Awais, Muzammal Naseer, Salman Khan, Rao\u00a0Muhammad Anwer, Hisham Cholakkal, Mubarak Shah, Ming-Hsuan Yang, and Fahad\u00a0Shahbaz Khan. 2023. Foundational models defining a new era in vision: A survey and outlook. arXiv preprint arXiv:2307.13721 (2023)."},{"key":"e_1_3_2_1_3_1","volume-title":"Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications. arXiv preprint arXiv:2404.13506","author":"Chandra\u00a0Sai Balne Charith","year":"2024","unstructured":"Charith Chandra\u00a0Sai Balne, Sreyoshi Bhaduri, Tamoghna Roy, Vinija Jain, and Aman Chadha. 2024. Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications. arXiv preprint arXiv:2404.13506 (2024)."},{"key":"e_1_3_2_1_4_1","volume-title":"Strong Baselines for Parameter-Efficient Few-Shot Fine-Tuning. AAAI Conference on Artificial Intelligence","author":"Basu Samyadeep","year":"2024","unstructured":"Samyadeep Basu, Shell Hu, Daniela Massiceti, and Soheil Feizi. 2024. Strong Baselines for Parameter-Efficient Few-Shot Fine-Tuning. AAAI Conference on Artificial Intelligence (2024)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3460421.3480427"},{"key":"e_1_3_2_1_6_1","volume-title":"AttX: Attentive cross-connections for fusion of wearable signals in emotion recognition. ACM Transactions on Computing for Healthcare","author":"Bhatti Anubhav","year":"2024","unstructured":"Anubhav Bhatti, Behnam Behinaein, Paul Hungler, and Ali Etemad. 2024. AttX: Attentive cross-connections for fusion of wearable signals in emotion recognition. ACM Transactions on Computing for Healthcare (2024)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/AIMHC59811.2024.00018"},{"key":"e_1_3_2_1_8_1","volume-title":"Interpreting forecasted vital signs using n-beats in sepsis patients. arXiv preprint arXiv:2306.14016","author":"Bhatti Anubhav","year":"2023","unstructured":"Anubhav Bhatti, Naveen Thangavelu, Marium Hassan, Choongmin Kim, San Lee, Yonghwan Kim, and Jang\u00a0Yong Kim. 2023. Interpreting forecasted vital signs using n-beats in sepsis patients. arXiv preprint arXiv:2306.14016 (2023)."},{"key":"e_1_3_2_1_9_1","volume-title":"On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258","author":"Bommasani Rishi","year":"2021","unstructured":"Rishi Bommasani, Drew\u00a0A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael\u00a0S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, 2021. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021)."},{"key":"e_1_3_2_1_10_1","volume-title":"International Conference on Pervasive Computing Technologies for Healthcare","author":"Bouton-Bessac Emma","year":"2022","unstructured":"Emma Bouton-Bessac, Lakmal Meegahapola, and Daniel Gatica-Perez. 2022. Your Day in Your Pocket: Complex Activity Recognition from Smartphone Accelerometers. International Conference on Pervasive Computing Technologies for Healthcare (2022)."},{"key":"e_1_3_2_1_11_1","volume-title":"Eduardus\u00a0JE Cottaar, Peter Andriessen, and Carola van Pul.","author":"Cabrera-Quiros Laura","year":"2021","unstructured":"Laura Cabrera-Quiros, Deedee Kommers, Maria\u00a0K Wolvers, Laurien Oosterwijk, Niek Arents, Jacqueline van\u00a0der Sluijs-Bens, Eduardus\u00a0JE Cottaar, Peter Andriessen, and Carola van Pul. 2021. Prediction of late-onset sepsis in preterm infants using monitoring signals and machine learning. Critical Care Explorations (2021)."},{"key":"e_1_3_2_1_12_1","volume-title":"Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity. Medical Imaging with Deep Learning","author":"Dutt Raman","year":"2024","unstructured":"Raman Dutt, Linus Ericsson, Pedro Sanchez, Sotirios\u00a0A Tsaftaris, and Timothy Hospedales. 2024. Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity. Medical Imaging with Deep Learning (2024)."},{"key":"e_1_3_2_1_13_1","volume-title":"arXiv preprint arXiv:2310.03589","author":"Garza Azul","year":"2023","unstructured":"Azul Garza and Max Mergenthaler-Canseco. 2023. TimeGPT-1. arXiv preprint arXiv:2310.03589 (2023)."},{"key":"e_1_3_2_1_14_1","volume-title":"Large language models are zero-shot time series forecasters. Advances in Neural Information Processing Systems","author":"Gruver Nate","year":"2024","unstructured":"Nate Gruver, Marc Finzi, Shikai Qiu, and Andrew\u00a0G Wilson. 2024. Large language models are zero-shot time series forecasters. Advances in Neural Information Processing Systems (2024)."},{"key":"e_1_3_2_1_15_1","volume-title":"Remote Heart Rate Monitoring in Smart Environments from Videos with Self-supervised Pre-training","author":"Gupta Divij","year":"2023","unstructured":"Divij Gupta and Ali Etemad. 2023. Remote Heart Rate Monitoring in Smart Environments from Videos with Self-supervised Pre-training. IEEE Internet of Things Journal (2023)."},{"key":"e_1_3_2_1_16_1","volume-title":"International Conference on Machine Learning","author":"Houlsby Neil","year":"2019","unstructured":"Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De\u00a0Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019. Parameter-efficient transfer learning for NLP. International Conference on Machine Learning (2019)."},{"key":"e_1_3_2_1_17_1","volume-title":"LoRA: Low-Rank Adaptation of Large Language Models. International Conference on Learning Representations","author":"Hu J","year":"2021","unstructured":"Edward\u00a0J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, 2021. LoRA: Low-Rank Adaptation of Large Language Models. International Conference on Learning Representations (2021)."},{"key":"e_1_3_2_1_18_1","volume-title":"AdvLoRA: Adversarial Low-Rank Adaptation of Vision-Language Models. arXiv preprint arXiv:2404.13425","author":"Ji Yuheng","year":"2024","unstructured":"Yuheng Ji, Yue Liu, Zhicheng Zhang, Zhao Zhang, Yuting Zhao, Gang Zhou, Xingwei Zhang, Xinwang Liu, and Xiaolong Zheng. 2024. AdvLoRA: Adversarial Low-Rank Adaptation of Vision-Language Models. arXiv preprint arXiv:2404.13425 (2024)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i21.30383"},{"key":"e_1_3_2_1_20_1","volume-title":"Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning. Findings of the Association for Computational Linguistics","author":"Lin Zhaojiang","year":"2020","unstructured":"Zhaojiang Lin, Andrea Madotto, and Pascale Fung. 2020. Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning. Findings of the Association for Computational Linguistics (2020)."},{"key":"e_1_3_2_1_21_1","volume-title":"Foundation and large language models: fundamentals, challenges, opportunities, and social impacts. Cluster Computing","author":"Myers Devon","year":"2024","unstructured":"Devon Myers, Rami Mohawesh, Venkata\u00a0Ishwarya Chellaboina, Anantha\u00a0Lakshmi Sathvik, Praveen Venkatesh, Yi-Hui Ho, Hanna Henshaw, Muna Alhawawreh, David Berdik, and Yaser Jararweh. 2024. Foundation and large language models: fundamentals, challenges, opportunities, and social impacts. Cluster Computing (2024)."},{"key":"e_1_3_2_1_22_1","volume-title":"The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Scientific data","author":"Pollard J","year":"2018","unstructured":"Tom\u00a0J Pollard, Alistair\u00a0EW Johnson, Jesse\u00a0D Raffa, Leo\u00a0A Celi, Roger\u00a0G Mark, and Omar Badawi. 2018. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Scientific data (2018)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3577190.3614113"},{"key":"e_1_3_2_1_24_1","volume":"202","author":"Raffel Colin","unstructured":"Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter\u00a0J Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research (2020).","journal-title":"J Liu."},{"key":"e_1_3_2_1_25_1","volume-title":"Lag-llama: Towards foundation models for time series forecasting. arXiv preprint arXiv:2310.08278","author":"Rasul Kashif","year":"2023","unstructured":"Kashif Rasul, Arjun Ashok, Andrew\u00a0Robert Williams, Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Marin Bilo\u0161, Hena Ghonia, Nadhir\u00a0Vincent Hassen, Anderson Schneider, 2023. Lag-llama: Towards foundation models for time series forecasting. arXiv preprint arXiv:2310.08278 (2023)."},{"key":"e_1_3_2_1_26_1","volume-title":"AdapterDrop: On the Efficiency of Adapters in Transformers. Conference on Empirical Methods in Natural Language Processing","author":"R\u00fcckl\u00e9 Andreas","year":"2021","unstructured":"Andreas R\u00fcckl\u00e9, Gregor Geigle, Max Glockner, Tilman Beck, Jonas Pfeiffer, Nils Reimers, and Iryna Gurevych. 2021. AdapterDrop: On the Efficiency of Adapters in Transformers. Conference on Empirical Methods in Natural Language Processing (2021)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20053-3_29"},{"key":"e_1_3_2_1_28_1","volume-title":"Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971","author":"Touvron Hugo","year":"2023","unstructured":"Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth\u00e9e Lacroix, Baptiste Rozi\u00e8re, Naman Goyal, Eric Hambro, Faisal Azhar, 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)."},{"key":"e_1_3_2_1_29_1","volume-title":"Attention is all you need. Advances in Neural Information Processing Systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan\u00a0N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems (2017)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2024.3368703"},{"key":"e_1_3_2_1_31_1","volume-title":"Unified training of universal time series forecasting transformers. arXiv preprint arXiv:2402.02592","author":"Woo Gerald","year":"2024","unstructured":"Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, and Doyen Sahoo. 2024. Unified training of universal time series forecasting transformers. arXiv preprint arXiv:2402.02592 (2024)."},{"key":"e_1_3_2_1_32_1","volume-title":"BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models. Annual Meeting of the Association for Computational Linguistics","author":"Zaken Elad\u00a0Ben","year":"2022","unstructured":"Elad\u00a0Ben Zaken, Yoav Goldberg, and Shauli Ravfogel. 2022. BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models. Annual Meeting of the Association for Computational Linguistics (2022)."}],"event":{"name":"ICMI '24: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION","location":"San Jose Costa Rica","acronym":"ICMI '24"},"container-title":["International Conference on Multimodel Interaction"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3678957.3685724","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3678957.3685724","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:10:12Z","timestamp":1750295412000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3678957.3685724"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,4]]},"references-count":32,"alternative-id":["10.1145\/3678957.3685724","10.1145\/3678957"],"URL":"https:\/\/doi.org\/10.1145\/3678957.3685724","relation":{},"subject":[],"published":{"date-parts":[[2024,11,4]]},"assertion":[{"value":"2024-11-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}