2 code implementations • 1 Mar 2021 • Haoran You, Zhihan Lu, Zijian Zhou, Yonggan Fu, Yingyan Lin
Experiments on various GCN models and datasets consistently validate our GEB finding and the effectiveness of our GEBT, e. g., our GEBT achieves up to 80. 2% ~ 85. 6% and 84. 6% ~ 87. 5% savings of GCN training and inference costs while offering a comparable or even better accuracy as compared to state-of-the-art methods.
no code implementations • 7 Jan 2021 • Haoran You, Randall Balestriero, Zhihan Lu, Yutong Kou, Huihong Shi, Shunyao Zhang, Shang Wu, Yingyan Lin, Richard Baraniuk
In this paper, we study the importance of pruning in Deep Networks (DNs) and the yin & yang relationship between (1) pruning highly overparametrized DNs that have been trained from random initialization and (2) training small DNs that have been "cleverly" initialized.
1 code implementation • ICCV 2021 • Yonggan Fu, Yang Zhang, Yue Wang, Zhihan Lu, Vivek Boominathan, Ashok Veeraraghavan, Yingyan Lin
PhlatCam, with its form factor potentially reduced by orders of magnitude, has emerged as a promising solution to the first aforementioned challenge, while the second one remains a bottleneck.