126 citations · 374 across the 49 of their papers we have counts for
4 papers · 1 filter
Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion
Hongxu Yin, Pavlo Molchanov, Zhizhong Li +5
We introduce DeepInversion, a new method for synthesizing images from the image distribution used to train a deep neural network. We 'invert' a trained network (teacher) to synthes…
DiabDeep: Pervasive Diabetes Diagnosis based on Wearable Medical Sensors and Efficient Neural Networks
Hongxu Yin, Bilal Mukadam, Xiaoliang Dai +1
Diabetes impacts the quality of life of millions of people. However, diabetes diagnosis is still an arduous process, given that the disease develops and gets treated outside the cl…
Incremental Learning Using a Grow-and-Prune Paradigm with Efficient Neural Networks
Xiaoliang Dai, Hongxu Yin, Niraj K. Jha
Deep neural networks (DNNs) have become a widely deployed model for numerous machine learning applications. However, their fixed architecture, substantial training cost, and signif…
Hardware-Guided Symbiotic Training for Compact, Accurate, yet Execution-Efficient LSTM
Hongxu Yin, Guoyang Chen, Yingmin Li +3
Many long short-term memory (LSTM) applications need fast yet compact models. Neural network compression approaches, such as the grow-and-prune paradigm, have proved to be promisin…