6.5k citations · 10.4k across the 118 of their papers we have counts for
16 papers · 1 filter
PyHessian: Neural Networks Through the Lens of the Hessian
Zhewei Yao, Amir Gholami, Kurt Keutzer +1
We present PYHESSIAN, a new scalable framework that enables fast computation of Hessian (i.e., second-order derivative) information for deep neural networks. PYHESSIAN enables fast…
Domain-Aware Dynamic Networks
Tianyuan Zhang, Bichen Wu, Xin Wang +2
Deep neural networks with more parameters and FLOPs have higher capacity and generalize better to diverse domains. But to be deployed on edge devices, the model's complexity has to…
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks
Zhen Dong, Zhewei Yao, Yaohui Cai +4
Quantization is an effective method for reducing memory footprint and inference time of Neural Networks, e.g., for efficient inference in the cloud, especially at the edge. However…
Multi-source Distilling Domain Adaptation
Sicheng Zhao, Guangzhi Wang, Shanghang Zhang +7
Deep neural networks suffer from performance decay when there is domain shift between the labeled source domain and unlabeled target domain, which motivates the research on domain…
Multi-source Domain Adaptation for Semantic Segmentation
Sicheng Zhao, Bo Li, Xiangyu Yue +5
Simulation-to-real domain adaptation for semantic segmentation has been actively studied for various applications such as autonomous driving. Existing methods mainly focus on a sin…
Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization
Paras Jain, Ajay Jain, Aniruddha Nrusimha +5
We formalize the problem of trading-off DNN training time and memory requirements as the tensor rematerialization optimization problem, a generalization of prior checkpointing stra…