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We present SensorLM, a family of sensor-language foundation models that enable wearable sensor data understanding with natural language.
Bootstrap confidence intervals: when, which, what? a practical guide for medical statisticians
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Visualizing data using t-sne
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Decoupled weight decay regularization
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Fitbit-based interventions for healthy lifestyle outcomes: systematic review and meta-analysis
Mickael Ringeval, Gerit Wagner, James Denford, Guy Paré, and Spyros Kitsiou · 2020
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Exploring contrastive learning in human activity recognition for healthcare
Chi Ian Tang, Ignacio Perez-Pozuelo, Dimitris Spathis, and Cecilia Mascolo · 2020
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Simvlm: Simple visual language model pretraining with weak supervision
Zirui Wang, Jiahui Yu, Adams Wei Yu, Zihang Dai, Yulia Tsvetkov, and Yuan Cao · 2021
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Deepsqa: Understanding sensor data via question answering
Tianwei Xing, Luis Garcia, Federico Cerutti, Lance Kaplan, Alun Preece, and Mani Srivastava · 2021
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Masked siamese networks for label-efficient learning
Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Mike Rabbat, and Nicolas Ballas · 2022
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Data augmentation for learning predictive models on eeg: a systematic comparison
Cédric Rommel, Joseph Paillard, Thomas Moreau, and Alexandre Gramfort · 2022
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Artificial intelligence-enabled detection and assessment of parkinson’s disease using nocturnal breathing signals
Yuzhe Yang, Yuan Yuan, Guo Zhang, Hao Wang, Ying-Cong Chen, Yingcheng Liu, Christopher G Tarolli, Daniel Crepeau, Jan Bukartyk, Mithri R Junna, et al · 2022
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Coca: Contrastive captioners are image-text foundation models
Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu · 2022
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Self-supervised contrastive pre-training for time series via time-frequency consistency
Xiang Zhang, Ziyuan Zhao, Theodoros Tsiligkaridis, and Marinka Zitnik · 2022
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Large-scale training of foundation models for wearable biosignals
Salar Abbaspourazad, Oussama Elachqar, Andrew Miller, Saba Emrani, Udhyakumar Nallasamy, and Ian Shapiro · 2023
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Clap learning audio concepts from natural language supervision
Benjamin Elizalde, Soham Deshmukh, Mahmoud Al Ismail, and Huaming Wang · 2023
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A visual–language foundation model for pathology image analysis using medical twitter
Zhi Huang, Federico Bianchi, Mert Yuksekgonul, Thomas J Montine, and James Zou · 2023
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Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi · 2023
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Health-llm: Large language models for health prediction via wearable sensor data
Yubin Kim, Xuhai Xu, Daniel McDuff, Cynthia Breazeal, and Hae Won Park · 2024
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Guidelines for augmentation selection in contrastive learning for time series classification
Ziyu Liu, Azadeh Alavi, Minyi Li, and Xiang Zhang · 2024
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A visual-language foundation model for computational pathology
Ming Y Lu, Bowen Chen, Drew FK Williamson, Richard J Chen, Ivy Liang, Tong Ding, Guillaume Jaume, Igor Odintsov, Long Phi Le, Georg Gerber, et al · 2024
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Transforming wearable data into health insights using large language model agents
Mike A Merrill, Akshay Paruchuri, Naghmeh Rezaei, Geza Kovacs, Javier Perez, Yun Liu, Erik Schenck, Nova Hammerquist, Jake Sunshine, Shyam Tailor, et al · 2024
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Xin Liu, Daniel McDuff, Geza Kovacs, Isaac Galatzer-Levy, Jacob Sunshine, Jiening Zhan, Ming-Zher Poh, Shun Liao, Paolo Di Achille, and Shwetak Patel · 2023
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Imu2clip: language-grounded motion sensor translation with multimodal contrastive learning
Seungwhan Moon, Andrea Madotto, Zhaojiang Lin, Aparajita Saraf, Amy Bearman, and Babak Damavandi · 2023
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Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Image captioners are scalable vision learners too
Michael Tschannen, Manoj Kumar, Andreas Steiner, Xiaohua Zhai, Neil Houlsby, and Lucas Beyer · 2023
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Simper: Simple self-supervised learning of periodic targets
Yuzhe Yang, Xin Liu, Jiang Wu, Silviu Borac, Dina Katabi, Ming-Zher Poh, and Daniel McDuff · 2023
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A foundation model for generalizable disease detection from retinal images
Yukun Zhou, Mark A Chia, Siegfried K Wagner, Murat S Ayhan, Dominic J Williamson, Robbert R Struyven, Timing Liu, Moucheng Xu, Mateo G Lozano, Peter Woodward-Court, et al · 2023
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Mike A. Merrill, Mingtian Tan, Vinayak Gupta, Thomas Hartvigsen, and Tim Althoff · 2024
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SleepFM: Multi-modal representation learning for sleep across brain activity, ECG and respiratory signals
Rahul Thapa, Bryan He, Magnus Ruud Kjaer, Hyatt Moore IV, Gauri Ganjoo, Emmanuel Mignot, and James Zou · 2024
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Self-supervised learning for human activity recognition using 700,000 person-days of wearable data
Hang Yuan, Shing Chan, Andrew P Creagh, Catherine Tong, Aidan Acquah, David A Clifton, and Aiden Doherty · 2024
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Unimts: Unified pre-training for motion time series
Xiyuan Zhang, Diyan Teng, Ranak Roy Chowdhury, Shuheng Li, Dezhi Hong, Rajesh Gupta, and Jingbo Shang · 2024
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Ecg-chat: A large ecg-language model for cardiac disease diagnosis
Yubao Zhao, Tian Zhang, Xu Wang, Puyu Han, Tong Chen, Linlin Huang, Youzhu Jin, and Jiaju Kang · 2024
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Neurolm: A universal multi-task foundation model for bridging the gap between language and eeg signals
Weibang Jiang, Yansen Wang, Bao-liang Lu, and Dongsheng Li · 2025
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SensorLLM: Aligning large language models with motion sensors for human activity recognition, 2025
Zechen Li, Shohreh Deldari, Linyao Chen, Hao Xue, and Flora D. Salim · 2025
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Scaling wearable foundation models
Girish Narayanswamy, Xin Liu, Kumar Ayush, Yuzhe Yang, Xuhai Xu, shun liao, Jake Garrison, Shyam A. Tailor, Jacob Sunshine, Yun Liu, Tim Althoff, Shrikanth Narayanan, Pushmeet Kohli, Jiening Zhan, Mark Malhotra, Shwetak Patel, Samy Abdel-Ghaffar, and Daniel McDuff · 2025
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Gemma Team, Aishwarya Kamath, Johan Ferret, Shreya Pathak, Nino Vieillard, Ramona Merhej, Sarah Perrin, Tatiana Matejovicova, Alexandre Ramé, Morgane Rivière, et al · 2025
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Xiaofan Yu, Lanxiang Hu, Benjamin Reichman, Dylan Chu, Rushil Chandrupatla, Xiyuan Zhang, Larry Heck, and Tajana Rosing · 2025
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