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pro vyhledávání: '"Zhou, Ao"'
The discovery of drug-target interactions (DTIs) plays a crucial role in pharmaceutical development. The deep learning model achieves more accurate results in DTI prediction due to its ability to extract robust and expressive features from drug and t
Externí odkaz:
http://arxiv.org/abs/2404.10561
Autor:
Qiao, Tong, Yang, Jianlei, Qi, Yingjie, Zhou, Ao, Bai, Chen, Yu, Bei, Zhao, Weisheng, Hu, Chunming
Graph Neural Networks (GNNs) succeed significantly in many applications recently. However, balancing GNNs training runtime cost, memory consumption, and attainable accuracy for various applications is non-trivial. Previous training methodologies suff
Externí odkaz:
http://arxiv.org/abs/2404.09544
The key to device-edge co-inference paradigm is to partition models into computation-friendly and computation-intensive parts across the device and the edge, respectively. However, for Graph Neural Networks (GNNs), we find that simply partitioning wi
Externí odkaz:
http://arxiv.org/abs/2404.05605
Deep neural network models have demonstrated their effectiveness in classifying multi-label data from various domains. Typically, they employ a training mode that combines mini-batches with optimizers, where each sample is randomly selected with equa
Externí odkaz:
http://arxiv.org/abs/2403.18192
We explore the possibilities of categorizing $SU(3)_f$ representations of scalar mesons through $J/\psi\to SV$ and $\gamma S$, with $S$ ($V$) being the scalar(vector) mesons. We find that $f_0(500)$ and $f_0(980)$ are singlet and octet states, respec
Externí odkaz:
http://arxiv.org/abs/2403.07701
Communication overhead is a significant bottleneck in federated learning (FL), which has been exaggerated with the increasing size of AI models. In this paper, we propose FedRDMA, a communication-efficient cross-silo FL system that integrates RDMA in
Externí odkaz:
http://arxiv.org/abs/2403.00881
In the wake of the rapid deployment of large-scale low-Earth orbit satellite constellations, exploiting the full computing potential of Commercial Off-The-Shelf (COTS) devices in these environments has become a pressing issue. However, understanding
Externí odkaz:
http://arxiv.org/abs/2401.03435
In recent years, Low Earth Orbit (LEO) satellites have witnessed rapid development, with inference based on Deep Neural Network (DNN) models emerging as the prevailing technology for remote sensing satellite image recognition. However, the substantia
Externí odkaz:
http://arxiv.org/abs/2311.13509
Graph neural networks (GNNs) have gained significant popularity due to the powerful capability to extract useful representations from graph data. As the need for efficient GNN computation intensifies, a variety of programming abstractions designed fo
Externí odkaz:
http://arxiv.org/abs/2310.12184
Large Language Models (LLMs) such as GPTs and LLaMa have ushered in a revolution in machine intelligence, owing to their exceptional capabilities in a wide range of machine learning tasks. However, the transition of LLMs from data centers to edge dev
Externí odkaz:
http://arxiv.org/abs/2308.14352