6 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…
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…
Foundation Model Hidden Representations for Heart Rate Estimation from Auscultation
Jingping Nie, Dung T. Tran, Karan Thakkar +5
Auscultation, particularly heart sound, is a non-invasive technique that provides essential vital sign information. Recently, self-supervised acoustic representation foundation mod…
Modeling speech emotion with label variance and analyzing performance across speakers and unseen acoustic conditions
Vikramjit Mitra, Amrit Romana, Dung T. Tran +1
Spontaneous speech emotion data usually contain perceptual grades where graders assign emotion score after listening to the speech files. Such perceptual grades introduce uncertain…
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…