Zobrazeno 1 - 10
of 331
pro vyhledávání: '"Dun, Chen"'
Autor:
Dun, Chen, Pan, Qiutai, Jin, Shikai, Stevens, Ria, Miller, Mitchell D., Phillips, Jr., George N., Kyrillidis, Anastasios
Determining the structure of a protein has been a decades-long open question. A protein's three-dimensional structure often poses nontrivial computation costs, when classical simulation algorithms are utilized. Advances in the transformer neural netw
Externí odkaz:
http://arxiv.org/abs/2310.03899
Autor:
Dun, Chen, Garcia, Mirian Hipolito, Zheng, Guoqing, Awadallah, Ahmed Hassan, Kyrillidis, Anastasios, Sim, Robert
Large Language Models (LLMs) have the ability to solve a variety of tasks, such as text summarization and mathematical questions, just out of the box, but they are often trained with a single task in mind. Due to high computational costs, the current
Externí odkaz:
http://arxiv.org/abs/2310.02842
Advances in Semi-Supervised Learning (SSL) have almost entirely closed the gap between SSL and Supervised Learning at a fraction of the number of labels. However, recent performance improvements have often come \textit{at the cost of significantly in
Externí odkaz:
http://arxiv.org/abs/2309.03469
Autor:
Dun, Chen, Garcia, Mirian Hipolito, Zheng, Guoqing, Awadallah, Ahmed Hassan, Sim, Robert, Kyrillidis, Anastasios, Dimitriadis, Dimitrios
One of the goals in Federated Learning (FL) is to create personalized models that can adapt to the context of each participating client, while utilizing knowledge from a shared global model. Yet, often, personalization requires a fine-tuning step usi
Externí odkaz:
http://arxiv.org/abs/2306.08586
Recent work on the Lottery Ticket Hypothesis (LTH) shows that there exist ``\textit{winning tickets}'' in large neural networks. These tickets represent ``sparse'' versions of the full model that can be trained independently to achieve comparable acc
Externí odkaz:
http://arxiv.org/abs/2210.16169
Asynchronous learning protocols have regained attention lately, especially in the Federated Learning (FL) setup, where slower clients can severely impede the learning process. Herein, we propose \texttt{AsyncDrop}, a novel asynchronous FL framework t
Externí odkaz:
http://arxiv.org/abs/2210.16105
Federated learning enables many local devices to train a deep learning model jointly without sharing the local data. Currently, most of federated training schemes learns a global model by averaging the parameters of local models. However, most of the
Externí odkaz:
http://arxiv.org/abs/2110.12292
We propose ResIST, a novel distributed training protocol for Residual Networks (ResNets). ResIST randomly decomposes a global ResNet into several shallow sub-ResNets that are trained independently in a distributed manner for several local iterations,
Externí odkaz:
http://arxiv.org/abs/2107.00961
Autor:
Zhang, Huiyu, Xiang, Yanhong, Liu, Baocheng, Li, Guang, Dun, Chen, Huang, Haoyu, Zou, Qiuling, Xiong, Lizhi, Wu, Xianwen
Publikováno v:
In Journal of Colloid And Interface Science May 2024 661:389-400
Autor:
Sabharwal, Jasdeep, Dai, Xi, Dun, Chen, Chen, Ariel, Ali, Muhammad, Schein, Oliver D., Ramulu, Pradeep Y., Makary, Martin, Johnson, Thomas V., Woreta, Fasika
Publikováno v:
In Ophthalmology February 2024 131(2):179-187