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cs.LG2025
MultiTab: A Scalable Foundation for Multitask Learning on Tabular Data
Dimitrios Sinodinos, Jack Yi Wei, Narges Armanfard
Tabular data is the most abundant data type in the world, powering systems in finance, healthcare, e-commerce, and beyond. As tabular datasets grow and span multiple related target…
cs.CV2025
Zero-Shot Anomaly Detection with Dual-Branch Prompt Selection
Zihan Wang, Samira Ebrahimi Kahou, Narges Armanfard
Zero-shot anomaly detection (ZSAD) enables identifying and localizing defects in unseen categories by relying solely on generalizable features rather than requiring any labeled exa…