Zobrazeno 1 - 10
of 313
pro vyhledávání: '"Song, Yuqi"'
Convolutional neural networks (CNNs) have gained increasing popularity and versatility in recent decades, finding applications in diverse domains. These remarkable achievements are greatly attributed to the support of extensive datasets with precise
Externí odkaz:
http://arxiv.org/abs/2407.17630
Autor:
Wei, Lai, Omee, Sadman Sadeed, Dong, Rongzhi, Fu, Nihang, Song, Yuqi, Siriwardane, Edirisuriya M. D., Xu, Meiling, Wolverton, Chris, Hu, Jianjun
Crystal structure prediction (CSP) is now increasingly used in discovering novel materials with applications in diverse industries. However, despite decades of developments and significant progress in this area, there lacks a set of well-defined benc
Externí odkaz:
http://arxiv.org/abs/2407.00733
Computational prediction of stable crystal structures has a profound impact on the large-scale discovery of novel functional materials. However, predicting the crystal structure solely from a material's composition or formula is a promising yet chall
Externí odkaz:
http://arxiv.org/abs/2404.04810
Multi-label classification is a widely encountered problem in daily life, where an instance can be associated with multiple classes. In theory, this is a supervised learning method that requires a large amount of labeling. However, annotating data is
Externí odkaz:
http://arxiv.org/abs/2308.00166
Convolutional neural networks (CNNs) have been widely applied in many safety-critical domains, such as autonomous driving and medical diagnosis. However, concerns have been raised with respect to the trustworthiness of these models: The standard test
Externí odkaz:
http://arxiv.org/abs/2304.00697
Two-dimensional (2D) materials have wide applications in superconductors, quantum, and topological materials. However, their rational design is not well established, and currently less than 6,000 experimentally synthesized 2D materials have been repo
Externí odkaz:
http://arxiv.org/abs/2301.05824
Compared with multi-class classification, multi-label classification that contains more than one class is more suitable in real life scenarios. Obtaining fully labeled high-quality datasets for multi-label classification problems, however, is extreme
Externí odkaz:
http://arxiv.org/abs/2210.13651
Depth information is the foundation of perception, essential for autonomous driving, robotics, and other source-constrained applications. Promptly obtaining accurate and efficient depth information allows for a rapid response in dynamic environments.
Externí odkaz:
http://arxiv.org/abs/2210.13646
Self-supervised neural language models have recently found wide applications in generative design of organic molecules and protein sequences as well as representation learning for downstream structure classification and functional prediction. However
Externí odkaz:
http://arxiv.org/abs/2209.09406
Autor:
Fu, Nihang, Wei, Lai, Song, Yuqi, Li, Qinyang, Xin, Rui, Omee, Sadman Sadeed, Dong, Rongzhi, Siriwardane, Edirisuriya M. Dilanga, Hu, Jianjun
Pre-trained transformer language models on large unlabeled corpus have produced state-of-the-art results in natural language processing, organic molecule design, and protein sequence generation. However, no such models have been applied to learn the
Externí odkaz:
http://arxiv.org/abs/2206.13578