6 papers
RainODE: Continuous-Time Precipitation Forecasting with Latent Neural ODEs
Yeeun Seong, Doyi Kim, Minseok Seo +1
In precipitation forecasting, not only accuracy but also temporal resolution is critical. However, increasing temporal resolution is constrained by observational limitations and th…
Query-Conditioned Test-Time Self-Training for Large Language Models
Chaehee Song, Minseok Seo, Yeeun Seong +2
Large language models (LLMs) are typically deployed with fixed parameters, and their performance is often improved by allocating more computation at inference time. While such test…
Efficient Test-Time Optimization for Depth Completion via Low-Rank Decoder Adaptation
Minseok Seo, Wonjun Lee, Jaehyuk Jang +1
Zero-shot depth completion has gained attention for its ability to generalize across environments without sensor-specific datasets or retraining. However, most existing approaches…
Station2Radar: query conditioned gaussian splatting for precipitation field
Doyi Kim, Minseok Seo, Changick Kim
Precipitation forecasting relies on heterogeneous data. Weather radar is accurate, but coverage is geographically limited and costly to maintain. Weather stations provide accurate…
Data-driven Precipitation Nowcasting Using Satellite Imagery
Young-Jae Park, Doyi Kim, Minseok Seo +2
Accurate precipitation forecasting is crucial for early warnings of disasters, such as floods and landslides. Traditional forecasts rely on ground-based radar systems, which are sp…
Masked Autoregressive Model for Weather Forecasting
Doyi Kim, Minseok Seo, Hakjin Lee +1
The growing impact of global climate change amplifies the need for accurate and reliable weather forecasting. Traditional autoregressive approaches, while effective for temporal mo…