69 citations · 268 across the 20 of their papers we have counts for
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cs.LG2021★ 65 cited
On Representation Knowledge Distillation for Graph Neural Networks
Chaitanya K. Joshi, Fayao Liu, Xu Xun +2
Knowledge distillation is a learning paradigm for boosting resource-efficient graph neural networks (GNNs) using more expressive yet cumbersome teacher models. Past work on distill…
cs.LG2021★ 2 cited
A*HAR: A New Benchmark towards Semi-supervised learning for Class-imbalanced Human Activity Recognition
Govind Narasimman, Kangkang Lu, Arun Raja +4
Despite the vast literature on Human Activity Recognition (HAR) with wearable inertial sensor data, it is perhaps surprising that there are few studies investigating semisupervised…
cs.LG2019★ 4 cited
Dataflow-based Joint Quantization of Weights and Activations for Deep Neural Networks
Xue Geng, Jie Fu, Bin Zhao +4
This paper addresses a challenging problem - how to reduce energy consumption without incurring performance drop when deploying deep neural networks (DNNs) at the inference stage.…