1 citations · 1 across the 5 of their papers we have counts for
5 papers
Perseus: Interactive Time Series Segmentation with Sparse Supervision via Stateful Memory
Ching Chang, Ming-Chih Lo, Chiao-Tung Chan +2
Real-world systems, ranging from industrial manufacturing to wearable healthcare, generate multivariate time series with hierarchical states ranging from coarse regimes to fine-gra…
PromptTSS: A Prompting-Based Approach for Interactive Multi-Granularity Time Series Segmentation
Ching Chang, Ming-Chih Lo, Wen-Chih Peng +1
Multivariate time series data, collected across various fields such as manufacturing and wearable technology, exhibit states at multiple levels of granularity, from coarse-grained…
LLM-Based Routing in Mixture of Experts: A Novel Framework for Trading
Kuan-Ming Liu, Ming-Chih Lo
Recent advances in deep learning and large language models (LLMs) have facilitated the deployment of the mixture-of-experts (MoE) mechanism in the stock investment domain. While th…
Text2Freq: Learning Series Patterns from Text via Frequency Domain
Ming-Chih Lo, Ching Chang, Wen-Chih Peng
Traditional time series forecasting models mainly rely on historical numeric values to predict future outcomes.While these models have shown promising results, they often overlook…
Team Trifecta at Factify5WQA: Setting the Standard in Fact Verification with Fine-Tuning
Shang-Hsuan Chiang, Ming-Chih Lo, Lin-Wei Chao +1
In this paper, we present Pre-CoFactv3, a comprehensive framework comprised of Question Answering and Text Classification components for fact verification. Leveraging In-Context Le…