5 papers
Interweaving Marginals into Multivariate Sample Paths: Training-Free Dependence Construction for Probabilistic Time Series Foundation Models
Jinmyeong Choi, Jinkwan Jang, Seul Lee +1
Probabilistic time series foundation models (TSFMs) provide coordinate-wise predictive distributions, but these marginals do not determine a joint distribution over multivariate fu…
Beyond Case Law: Evaluating Structure-Aware Retrieval and Safety in Statute-Centric Legal QA
Kyubyung Chae, Jewon Yeom, Jeongjae Park +5
Legal QA benchmarks have predominantly focused on case law, overlooking the unique challenges of statute-centric regulatory reasoning. In statutory domains, relevant evidence is di…
What If TSF: Reframing Time Series Forecasting as Scenario-Guided Multimodal Forecasting
Jinkwan Jang, Hyunbin Jin, Hyungjin Park +2
Recent advances in large language models (LLMs) have enabled time series forecasting to move beyond numerical observations and incorporate external information in multimodal settin…
Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependency, Asynchrony, and Missingness
Jinkwan Jang, Hyungjin Park, Jinmyeong Choi +1
Real-world time series data are inherently multivariate, often exhibiting complex inter-channel dependencies. Each channel is typically sampled at its own period and is prone to mi…
When Vision Models Meet Parameter Efficient Look-Aside Adapters Without Large-Scale Audio Pretraining
Juan Yeo, Jinkwan Jang, Kyubyung Chae +2
Recent studies show that pretrained vision models can boost performance in audio downstream tasks. To enhance the performance further, an additional pretraining stage with large sc…