This year, submissions to the proceedings track will have the option to cross-submit to the #NeurIPS2025 Learning from Time Series for Health Workshop (https://t.co/3OaCjm5Q1o). More details here: https://t.co/lhv8812Qge #ML4H
this is my recent research at @Google on learning #foundationmodels for #wearable sensors in real-world settings with real-world missingness and noise!!!
Introducing LSM-2, our newest foundation model for wearable sensor data. LSM-2 uses Adaptive & Inherited Masking, a novel self-supervised framework, to learn from incomplete data & achieve strong performance without requiring explicit imputation. More → https://t.co/jeMvzVupZg
Introducing LSM-2, our newest foundation model for wearable sensor data. LSM-2 uses Adaptive & Inherited Masking, a novel self-supervised framework, to learn from incomplete data & achieve strong performance without requiring explicit imputation. More → https://t.co/jeMvzVupZg
If you are working on AI in healthcare, please consider submitting to our Time-series 4 Health workshop at NeurIPS 2025!! We have prepared a great lineup of speakers and talks
⌚️🏥⏱️📈🧠🫀💃
🩺📈 The "Learning from Time-series for Health (TS4H)" workshop is BACK at NeurIPS 2025 🥳!
This workshop unites researchers across health time-series domains (from wearables to clinical systems) to tackle shared challenges. Details: https://t.co/X2pw6WTX9h
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🩺📈 The "Learning from Time-series for Health (TS4H)" workshop is BACK at NeurIPS 2025 🥳!
This workshop unites researchers across health time-series domains (from wearables to clinical systems) to tackle shared challenges. Details: https://t.co/X2pw6WTX9h
🧵 (1/6)
Excited to be in beautiful Singapore for #ICLR2025! Come see our poster on RelCon, SOTA foundation model for motion data, led by stellar @maxxu05 with Apple Health Research. Poster 17 in session 6 (3:00 - 5:30 April 26 Hall 3+2B) https://t.co/fAIEJpWkhQ
Thank you so much to all of my amazing collaborators at Apple (Jaya, Greg, Haraldur, Shirley, @HyewonMandyJ, and many more I can't list) and my amazing PI, @RehgJim, for all of their endless help and support!!
My paper RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data, from my @Apple internship, has been accepted at #ICLR2025! 🎉
We introduce the first IMU foundation model, unlocking generalization across motion tasks. 🏃♀️📊
https://t.co/BGVzFjSjxn
Poster presentation alert 🚨
I will present the paper “Event-Based Contrastive Learning” in Poster Session B (Poster #156, 3:35-4:55PM) @mlforhc !
Link to camera-ready version of paper: https://t.co/IrqqcEer80
@RehgJim and I will be presenting this work in person at ICLR tomorrow (5/7) at Poster Session 2 @ 4:30 PM as Poster #156, and we will be here all week as well. Hope to see you all there!
#ICLR2024 How can we choose meaningful positive pairs for time-series contrastive learning? What about motif similarity?
REBAR uses a learned measure that captures motif similarity and achieves SOTA performance.
Arxiv: https://t.co/hvYfgZD1wO
Github: https://t.co/e5oNccvzlz
How can we fill in missing pulsative sensor data? Prior state-of-the-art fails in our novel setting, despite its well-defined temporal structure.
Checkout our #NeurIPS2022 paper, PulseImpute, @ 4 pm CST!
arxiv: https://t.co/Hbv2x7ZvkP
github: https://t.co/bTManuLyEH
Dense self-supervised learning from multiple 3D viewpoints → dense feature representations that generalize both to novel object instances and to novel categories of instances.
Checkout our #NeurIPS2022 paper!
arxiv: https://t.co/rxzdrScII1
github: https://t.co/5n98W7Wykt
Next up at #INSAR2022@maxxu05@RehgJim @Agamooz @ncrbrady applying a novel machine learning model to language development in autism. Discovering Novel Predictors of Minimally Verbal Outcomes in Autism through Computational Modeling. Thread by @RehgJim (1/7)