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pro vyhledávání: '"Li, Kenli"'
Elucidating the intricate relationship between the structure and dynamics in the context of the glass transition has been a persistent challenge. Machine learning (ML) has emerged as a pivotal tool, offering novel pathways to predict dynamic behavior
Externí odkaz:
http://arxiv.org/abs/2407.06111
Sparse tensors are prevalent in real-world applications, often characterized by their large-scale, high-order, and high-dimensional nature. Directly handling raw tensors is impractical due to the significant memory and computational overhead involved
Externí odkaz:
http://arxiv.org/abs/2404.10087
Autor:
Zhou, Peng, Wang, Jianmin, Li, Chunyan, Wang, Zixu, Liu, Yiping, Liu, Chubo, Sun, Siqi, Lin, Jianxin, Wei, Leyi, Cai, Xibao, Lai, Houtim, Liu, Wei, Wang, Longyue, Zeng, Xiangxiang, Li, Kenli
While various models and computational tools have been proposed for structure and property analysis of molecules, generating molecules that conform to all desired structures and properties remains a challenge. Here, we introduce a multi-constraint mo
Externí odkaz:
http://arxiv.org/abs/2403.13244
Single-domain generalized object detection aims to enhance a model's generalizability to multiple unseen target domains using only data from a single source domain during training. This is a practical yet challenging task as it requires the model to
Externí odkaz:
http://arxiv.org/abs/2402.01304
Machine unlearning techniques, which involve retracting data records and reducing influence of said data on trained models, help with the user privacy protection objective but incur significant computational costs. Weight perturbation-based unlearnin
Externí odkaz:
http://arxiv.org/abs/2401.04385
Unraveling the structural factors influencing the dynamics of amorphous solids is crucial. While deep learning aids in navigating these complexities, transparency issues persist. Inspired by the successful application of prototype neural networks in
Externí odkaz:
http://arxiv.org/abs/2401.03743
New categories may be introduced over time, or existing categories may need to be reclassified. Class incremental learning (CIL) is employed for the gradual acquisition of knowledge about new categories while preserving information about previously l
Externí odkaz:
http://arxiv.org/abs/2401.02457
While decentralized training is attractive in multi-agent reinforcement learning (MARL) for its excellent scalability and robustness, its inherent coordination challenges in collaborative tasks result in numerous interactions for agents to learn good
Externí odkaz:
http://arxiv.org/abs/2312.12095
Publikováno v:
The Journal of Chemical Physics, 159(14) (2023)
Understanding the dynamic processes of the glassy system continues to be challenging. Recent advances have shown the power of graph neural networks (GNNs) for determining the correlation between structure and dynamics in the glassy system. These meth
Externí odkaz:
http://arxiv.org/abs/2211.12832