50 citations · 82 across the 6 of their papers we have counts for
6 papers
Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting
Xinli Yu, Zheng Chen, Yuan Ling +3
This paper presents a novel study on harnessing Large Language Models' (LLMs) outstanding knowledge and reasoning abilities for explainable financial time series forecasting. The a…
Large-scale Hybrid Approach for Predicting User Satisfaction with Conversational Agents
Dookun Park, Hao Yuan, Dongmin Kim +10
Measuring user satisfaction level is a challenging task, and a critical component in developing large-scale conversational agent systems serving the needs of real users. An widely…
Neural Stochastic Block Model & Scalable Community-Based Graph Learning
Zheng Chen, Xinli Yu, Yuan Ling +1
This paper proposes a novel scalable community-based neural framework for graph learning. The framework learns the graph topology through the task of community detection and link p…
Pre-Training for Query Rewriting in A Spoken Language Understanding System
Zheng Chen, Xing Fan, Yuan Ling +2
Query rewriting (QR) is an increasingly important technique to reduce customer friction caused by errors in a spoken language understanding pipeline, where the errors originate fro…
Knowledge Distillation from Internal Representations
Gustavo Aguilar, Yuan Ling, Yu Zhang +3
Knowledge distillation is typically conducted by training a small model (the student) to mimic a large and cumbersome model (the teacher). The idea is to compress the knowledge fro…
Correlated Anomaly Detection from Large Streaming Data
Zheng Chen, Xinli Yu, Yuan Ling +4
Correlated anomaly detection (CAD) from streaming data is a type of group anomaly detection and an essential task in useful real-time data mining applications like botnet detection…