10 citations · 18 across the 18 of their papers we have counts for
6 papers · 1 filter
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…
Shapelets-Enriched Selective Forecasting using Time Series Foundation Models
Shivani Tomar, Seshu Tirupathi, Elizabeth Daly +1
Time series foundation models have recently gained a lot of attention due to their ability to model complex time series data encompassing different domains including traffic, energ…
Dynamic Features Adaptation in Networking: Toward Flexible training and Explainable inference
Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo +2
As AI becomes a native component of 6G network control, AI models must adapt to continuously changing conditions, including the introduction of new features and measurements driven…
Pre-Hoc Predictions in AutoML: Leveraging LLMs to Enhance Model Selection and Benchmarking for Tabular datasets
Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo +2
The field of AutoML has made remarkable progress in post-hoc model selection, with libraries capable of automatically identifying the most performing models for a given dataset. Ne…
Domain Adaptation for Time series Transformers using One-step fine-tuning
Subina Khanal, Seshu Tirupathi, Giulio Zizzo +2
The recent breakthrough of Transformers in deep learning has drawn significant attention of the time series community due to their ability to capture long-range dependencies. Howev…
Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks
Francesco Fusco, Bradley Eck, Robert Gormally +2
The transition away from carbon-based energy sources poses several challenges for the operation of electricity distribution systems. Increasing shares of distributed energy resourc…