44 citations · 165 across the 14 of their papers we have counts for
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cs.LG2018★ 11 cited
Improving the Interpretability of Deep Neural Networks with Knowledge Distillation
Xuan Liu, Xiaoguang Wang, Stan Matwin
Deep Neural Networks have achieved huge success at a wide spectrum of applications from language modeling, computer vision to speech recognition. However, nowadays, good performanc…
math.OC2018★ 1 cited
Compositional planning in Markov decision processes: Temporal abstraction meets generalized logic composition
Xuan Liu, Jie Fu
In hierarchical planning for Markov decision processes (MDPs), temporal abstraction allows planning with macro-actions that take place at different time scale in form of sequential…
cs.LG2018★ 12 cited
Interpretable Deep Convolutional Neural Networks via Meta-learning
Xuan Liu, Xiaoguang Wang, Stan Matwin
Model interpretability is a requirement in many applications in which crucial decisions are made by users relying on a model's outputs. The recent movement for "algorithmic fairnes…