Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Du, Xiaocong"'
Autor:
Du, Xiaocong, Zhang, Haipeng
Achieving gender equality is a pivotal factor in realizing the UN's Global Goals for Sustainable Development. Gender bias studies work towards this and rely on name-based gender inference tools to assign individual gender labels when gender informati
Externí odkaz:
http://arxiv.org/abs/2405.06221
Autor:
Ye, Mao, Jiang, Ruichen, Wang, Haoxiang, Choudhary, Dhruv, Du, Xiaocong, Bhushanam, Bhargav, Mokhtari, Aryan, Kejariwal, Arun, Liu, Qiang
One of the key challenges of learning an online recommendation model is the temporal domain shift, which causes the mismatch between the training and testing data distribution and hence domain generalization error. To overcome, we propose to learn a
Externí odkaz:
http://arxiv.org/abs/2209.01143
Autor:
Du, Xiaocong, Bhushanam, Bhargav, Yu, Jiecao, Choudhary, Dhruv, Gao, Tianxiang, Wong, Sherman, Feng, Louis, Park, Jongsoo, Cao, Yu, Kejariwal, Arun
Deep learning recommendation systems at scale have provided remarkable gains through increasing model capacity (i.e. wider and deeper neural networks), but it comes at significant training cost and infrastructure cost. Model pruning is an effective t
Externí odkaz:
http://arxiv.org/abs/2105.01064
Autor:
Ju, Yun, Zhang, Hongyu, Du, Xiaocong, Wei, Jingxuan, Liu, Jun, Wei, Liang, Liu, Qingdai, Xu, Ning
Publikováno v:
In Metabolic Engineering September 2023 79:182-191
Deep Neural Networks (DNNs) are usually over-parameterized, causing excessive memory and interconnection cost on the hardware platform. Existing pruning approaches remove secondary parameters at the end of training to reduce the model size; but witho
Externí odkaz:
http://arxiv.org/abs/1911.04453
Today a canonical approach to reduce the computation cost of Deep Neural Networks (DNNs) is to pre-define an over-parameterized model before training to guarantee the learning capacity, and then prune unimportant learning units (filters and neurons)
Externí odkaz:
http://arxiv.org/abs/1905.11533
There is an increasing need of continual learning in dynamic systems, such as the self-driving vehicle, the surveillance drone, and the robotic system. Such a system requires learning from the data stream, training the model to preserve previous info
Externí odkaz:
http://arxiv.org/abs/1905.11550
Deep Neural Networks (DNNs) on hardware is facing excessive computation cost due to the massive number of parameters. A typical training pipeline to mitigate over-parameterization is to pre-define a DNN structure first with redundant learning units (
Externí odkaz:
http://arxiv.org/abs/1905.11530
Machine learning algorithms have made significant advances in many applications. However, their hardware implementation on the state-of-the-art platforms still faces several challenges and are limited by various factors, such as memory volume, memory
Externí odkaz:
http://arxiv.org/abs/1906.08866
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