189 citations · 259 across the 3 of their papers we have counts for
6 papers
Large-Scale Generative Data-Free Distillation
Liangchen Luo, Mark Sandler, Zi Lin +2
Knowledge distillation is one of the most popular and effective techniques for knowledge transfer, model compression and semi-supervised learning. Most existing distillation approa…
MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning
Guangxiang Zhao, Xu Sun, Jingjing Xu +2
In sequence to sequence learning, the self-attention mechanism proves to be highly effective, and achieves significant improvements in many tasks. However, the self-attention mecha…
Adaptive Gradient Methods with Dynamic Bound of Learning Rate
Liangchen Luo, Yuanhao Xiong, Yan Liu +1
Adaptive optimization methods such as AdaGrad, RMSprop and Adam have been proposed to achieve a rapid training process with an element-wise scaling term on learning rates. Though p…
Text Assisted Insight Ranking Using Context-Aware Memory Network
Qi Zeng, Liangchen Luo, Wenhao Huang +1
Extracting valuable facts or informative summaries from multi-dimensional tables, i.e. insight mining, is an important task in data analysis and business intelligence. However, ran…
Learning Personalized End-to-End Goal-Oriented Dialog
Liangchen Luo, Wenhao Huang, Qi Zeng +2
Most existing works on dialog systems only consider conversation content while neglecting the personality of the user the bot is interacting with, which begets several unsolved iss…
An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation
Liangchen Luo, Jingjing Xu, Junyang Lin +2
Generating semantically coherent responses is still a major challenge in dialogue generation. Different from conventional text generation tasks, the mapping between inputs and resp…