7 papers
Device Passport: Enabling Spatio-Temporal Pretrained Models to Generalize Across Input Layouts
Geeling Chau, Ran Liu, Juri Minxha +5
New device layouts pose a challenging modeling problem due to the lack of large datasets for each specific layout. Biosignal foundation models offer a plausible solution if they ar…
How PARTs assemble into wholes: Learning the relative composition of images
Melika Ayoughi, Samira Abnar, Chen Huang +10
The composition of objects and their parts, along with object-object positional relationships, provides a rich source of information for representation learning. Hence, spatial-awa…
CPEP: Contrastive Pose-EMG Pre-training Enhances Gesture Generalization on EMG Signals
Wenhui Cui, Christopher Sandino, Hadi Pouransari +7
Hand gesture classification using high-quality structured data such as videos, images, and hand skeletons is a well-explored problem in computer vision. Leveraging low-power, cost-…
Learning the relative composition of EEG signals using pairwise relative shift pretraining
Christopher Sandino, Sayeri Lala, Geeling Chau +6
Self-supervised learning (SSL) offers a promising approach for learning electroencephalography (EEG) representations from unlabeled data, reducing the need for expensive annotation…
Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement
Gaurav Patel, Christopher Sandino, Behrooz Mahasseni +4
In this paper, we propose a framework for efficient Source-Free Domain Adaptation (SFDA) in the context of time-series, focusing on enhancing both parameter efficiency and data-sam…
Promoting cross-modal representations to improve multimodal foundation models for physiological signals
Ching Fang, Christopher Sandino, Behrooz Mahasseni +5
Many healthcare applications are inherently multimodal, involving several physiological signals. As sensors for these signals become more common, improving machine learning methods…