4 papers · 1 filter
MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling
Hsing-Huan Chung, Shijun Li, Yoav Wald +3
Multimodal irregular time series (MITS) consist of asynchronous and irregularly sampled observations from heterogeneous numerical and textual channels. In healthcare, for example,…
Massively Multimodal Foundation Models: A Framework for Capturing Interactions with Specialized Mixture-of-Experts
Xing Han, Hsing-Huan Chung, Joydeep Ghosh +2
Modern applications increasingly involve many heterogeneous input streams, such as clinical sensors, wearable device data, imaging, and text, each with distinct measurement models,…
Between Linear and Sinusoidal: Rethinking the Time Encoder in Dynamic Graph Learning
Hsing-Huan Chung, Shravan Chaudhari, Xing Han +3
Dynamic graph learning is essential for applications involving temporal networks and requires effective modeling of temporal relationships. Seminal attention-based models like TGAT…
Novel Node Category Detection Under Subpopulation Shift
Hsing-Huan Chung, Shravan Chaudhari, Yoav Wald +2
In real-world graph data, distribution shifts can manifest in various ways, such as the emergence of new categories and changes in the relative proportions of existing categories.…