Search Results for author:
Found 3 papers, 2 papers with code
Early-Bird GCNs: Graph-Network Co-Optimization Towards More Efficient GCN Training and Inference via Drawing Early-Bird Lottery Tickets
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.
Max-Affine Spline Insights Into Deep Network Pruning
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.
SACoD: Sensor Algorithm Co-Design Towards Efficient CNN-powered Intelligent PhlatCam
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.
