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Is Our Benchmark Enough? An Analysis of Continual Learning for MLLMs
Van-Tuan Tran, Shruthi Gowda, Merim Dzaferagic +1
Continual adaptation is essential for multimodal large language models (MLLMs) deployed across evolving domains, but the state-of-the-art MR-LoRA method highly relies on the assump…
PRIM: Meta-Learned Bayesian Root Cause Analysis
Christopher Lohse, Anish Dhir, Amadou Ba +3
Root cause analysis (RCA) in complex systems is challenging due to error propagation across multiple variables, the need for structural causal knowledge, and the computational cost…
UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models
Van-Tuan Tran, Hong-Hanh Nguyen-Le, Marco Ruffini +1
Heterogeneous LoRA-rank methods address system heterogeneity in federated fine-tuning of foundation models by assigning client-specific ranks based on computational capabilities. H…
msData: A Millisecond-Resolution Network Dataset for Advancing Time Series Foundation Models
Subina Khanal, Seshu Tirupathi, Merim Dzaferagic +2
Time series foundation models (TSFMs) require diverse, real-world datasets to adapt across varying domains and temporal frequencies. However, current large-scale datasets predomina…