5 papers · 1 filter
Transformers as In-Context Samplers: From Closed-Form Diffusion to Estimation-Free Sampling
Arman Adibi, Alireza Jafari, Mohammad Ghavamzadeh +1
A growing body of work establishes that large language models are not mere statistical memorizers, but are capable of in-context learning: performing inference at test time using o…
Understanding Key Features of Time Series Foundation Models from Epidemic Forecasting
Alireza Jafari, Judy Fox, Geoffrey C. Fox +2
Seasonal influenza infects millions of people and causes substantial morbidity and mortality in the United States each year, making accurate short-term forecasting a core public-he…
Leveraging Exogenous Signals for Hydrology Time Series Forecasting
Junyang He, Judy Fox, Alireza Jafari +2
Recent advances in time series research facilitate the development of foundation models. While many state-of-the-art time series foundation models have been introduced, few studies…
Deep Learning Foundation and Pattern Models: Challenges in Hydrological Time Series
Junyang He, Ying-Jung Chen, Alireza Jafari +2
There has been active investigation into deep learning approaches for time series analysis, including foundation models. However, most studies do not address significant scientific…
Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcasting
Alireza Jafari, Geoffrey Fox, John B. Rundle +2
Advancing the capabilities of earthquake nowcasting, the real-time forecasting of seismic activities remains a crucial and enduring objective aimed at reducing casualties. This mul…