most citedKnowledge Distillation from Internal Representations

23 citations · 32 across the 5 of their papers we have counts for

collaborators

5 papers

cs.HC20203 cited

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…

cs.SI20201 cited

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…

cs.CL20204 cited

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…

cs.CL201923 cited

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…

cs.LG20191 cited

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…