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pro vyhledávání: '"Li Tianrui"'
This paper presents a novel approach to glass composition screening through a self-supervised learning framework, addressing the challenges posed by glass transition temperature (Tg) prediction. Given the critical role of Tg in determining glass perf
Externí odkaz:
http://arxiv.org/abs/2410.24083
Unsupervised feature selection (UFS) has recently gained attention for its effectiveness in processing unlabeled high-dimensional data. However, existing methods overlook the intrinsic causal mechanisms within the data, resulting in the selection of
Externí odkaz:
http://arxiv.org/abs/2410.12224
Recognizing 3D point cloud plays a pivotal role in many real-world applications. However, deploying 3D point cloud deep learning model is vulnerable to adversarial attacks. Despite many efforts into developing robust model by adversarial training, th
Externí odkaz:
http://arxiv.org/abs/2409.14940
Accurate prediction of metro Origin-Destination (OD) flow is essential for the development of intelligent transportation systems and effective urban traffic management. Existing approaches typically either predict passenger outflow of departure stati
Externí odkaz:
http://arxiv.org/abs/2409.04942
Established sampling protocols for 3D point cloud learning, such as Farthest Point Sampling (FPS) and Fixed Sample Size (FSS), have long been recognized and utilized. However, real-world data often suffer from corrputions such as sensor noise, which
Externí odkaz:
http://arxiv.org/abs/2408.12062
Autor:
Zhao, Xiaole, Li, Linze, Xie, Chengxing, Zhang, Xiaoming, Jiang, Ting, Lin, Wenjie, Liu, Shuaicheng, Li, Tianrui
Transformer-based deep models for single image super-resolution (SISR) have greatly improved the performance of lightweight SISR tasks in recent years. However, they often suffer from heavy computational burden and slow inference due to the complex c
Externí odkaz:
http://arxiv.org/abs/2408.04158
Autor:
Xie, Chengxing, Zhang, Xiaoming, Li, Linze, Meng, Haiteng, Zhang, Tianlin, Li, Tianrui, Zhao, Xiaole
Efficient and lightweight single-image super-resolution (SISR) has achieved remarkable performance in recent years. One effective approach is the use of large kernel designs, which have been shown to improve the performance of SISR models while reduc
Externí odkaz:
http://arxiv.org/abs/2407.14340
Personalized Federated Continual Learning (PFCL) is a new practical scenario that poses greater challenges in sharing and personalizing knowledge. PFCL not only relies on knowledge fusion for server aggregation at the global spatial-temporal perspect
Externí odkaz:
http://arxiv.org/abs/2407.00113
Semi-supervised multi-label feature selection has recently been developed to solve the curse of dimensionality problem in high-dimensional multi-label data with certain samples missing labels. Although many efforts have been made, most existing metho
Externí odkaz:
http://arxiv.org/abs/2406.12193
Learning temporal dependencies among targets (TDT) benefits better time series forecasting, where targets refer to the predicted sequence. Although autoregressive methods model TDT recursively, they suffer from inefficient inference and error accumul
Externí odkaz:
http://arxiv.org/abs/2406.04777