Zobrazeno 1 - 10
of 146
pro vyhledávání: '"Zhu, Feiyu"'
Autor:
Guo, Xiuyuan, Xu, Chengqi, Guo, Guinan, Zhu, Feiyu, Cai, Changpeng, Wang, Peizhe, Wei, Xiaoming, Su, Junhao, Gao, Jialin
Currently, training large-scale deep learning models is typically achieved through parallel training across multiple GPUs. However, due to the inherent communication overhead and synchronization delays in traditional model parallelism methods, seamle
Externí odkaz:
http://arxiv.org/abs/2411.12780
Knowledge distillation is widely applied in various fundamental vision models to enhance the performance of compact models. Existing knowledge distillation methods focus on designing different distillation targets to acquire knowledge from teacher mo
Externí odkaz:
http://arxiv.org/abs/2408.11478
Traditional deep learning relies on end-to-end backpropagation for training, but it suffers from drawbacks such as high memory consumption and not aligning with biological neural networks. Recent advancements have introduced locally supervised learni
Externí odkaz:
http://arxiv.org/abs/2407.05638
Autor:
Su, Junhao, Cai, Changpeng, Zhu, Feiyu, He, Chenghao, Xu, Xiaojie, Guan, Dongzhi, Si, Chenyang
Deep neural networks conventionally employ end-to-end backpropagation for their training process, which lacks biological credibility and triggers a locking dilemma during network parameter updates, leading to significant GPU memory use. Supervised lo
Externí odkaz:
http://arxiv.org/abs/2407.05623
Autor:
Zhang, Yuming, Zhang, Shouxin, Wang, Peizhe, Zhu, Feiyu, Guan, Dongzhi, Su, Junhao, Liu, Jiabin, Cai, Changpeng
Deep neural networks (DNNs) typically employ an end-to-end (E2E) training paradigm which presents several challenges, including high GPU memory consumption, inefficiency, and difficulties in model parallelization during training. Recent research has
Externí odkaz:
http://arxiv.org/abs/2406.16633
Autor:
Zhu, Feiyu, Zhang, Yuming, Cai, Changpeng, He, Chenghao, Guo, Xiuyuan, Li, Jiao, Wang, Peizhe, Su, Junhao, Gao, Jialin
Local learning offers an alternative to traditional end-to-end back-propagation in deep neural networks, significantly reducing GPU memory usage. While local learning has shown promise in image classification tasks, its application to other visual ta
Externí odkaz:
http://arxiv.org/abs/2406.00446
Autor:
Cai, Changpeng, Guo, Guinan, Li, Jiao, Su, Junhao, Shen, Fei, He, Chenghao, Xiao, Jing, Chen, Yuanxu, Dai, Lei, Zhu, Feiyu
Most earlier researches on talking face generation have focused on the synchronization of lip motion and speech content. However, head pose and facial emotions are equally important characteristics of natural faces. While audio-driven talking face ge
Externí odkaz:
http://arxiv.org/abs/2405.07257
Autor:
Zhu, Feiyu, Simmons, Reid
Large language models contain noisy general knowledge of the world, yet are hard to train or fine-tune. On the other hand cognitive architectures have excellent interpretability and are flexible to update but require a lot of manual work to instantia
Externí odkaz:
http://arxiv.org/abs/2403.00810
Publikováno v:
In Journal of Building Engineering 15 November 2024 97
Autor:
Huang, Jiateng, Zhu, Feiyu, Hu, Wei, Xie, Qiunan, Li, Xiaohan, Fei, Xiaoma, Liu, Jingcheng, Li, Xiaojie, Wei, Wei
Publikováno v:
In Reactive and Functional Polymers November 2024 204