4 papers
Representing Time Series as Structured Programs for LLM Reasoning
Jaeho Kim, Changhun Oh, Seokhyun Lee +2
Large language models (LLMs) have demonstrated strong reasoning and instruction-following capabilities, making them potentially powerful tools for time-series analysis. However, ti…
TimeTok: Granularity-Controllable Time-Series Generation via Hierarchical Tokenization
Seokhyun Lee, Jaeho Kim, Changjun Oh +2
Time-series generative models often lack control over temporal granularity, forcing users to accept whatever granularity the model produces. To enable truly user-driven generation,…
TransPL: VQ-Code Transition Matrices for Pseudo-Labeling of Time Series Unsupervised Domain Adaptation
Jaeho Kim, Seulki Lee
Unsupervised domain adaptation (UDA) for time series data remains a critical challenge in deep learning, with traditional pseudo-labeling strategies failing to capture temporal pat…
Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards
Jaeho Kim, Yunseok Lee, Seulki Lee
The peer review process in major artificial intelligence (AI) conferences faces unprecedented challenges with the surge of paper submissions (exceeding 10,000 submissions per venue…