6 citations · 6 across the 3 of their papers we have counts for
8 papers · 1 filter
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…
Achieving Fairness Across Local and Global Models in Federated Learning
Disha Makhija, Xing Han, Joydeep Ghosh +1
Achieving fairness across diverse clients in Federated Learning (FL) remains a significant challenge due to the heterogeneity of the data and the inaccessibility of sensitive attri…
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.…
FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion
Xing Han, Huy Nguyen, Carl Harris +2
As machine learning models in critical fields increasingly grapple with multimodal data, they face the dual challenges of handling a wide array of modalities, often incomplete due…
Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering
Xing Han, Tongzheng Ren, Jing Hu +2
We propose a novel approach to the problem of clustering hierarchically aggregated time-series data, which has remained an understudied problem though it has several commercial app…