Zobrazeno 1 - 10
of 11 770
pro vyhledávání: '"Keli, SO"'
In this work, we discover that causal inference provides a promising approach to capture heterophilic message-passing in Graph Neural Network (GNN). By leveraging cause-effect analysis, we can discern heterophilic edges based on asymmetric node depen
Externí odkaz:
http://arxiv.org/abs/2411.13821
This study presents wall-resolved large-eddy simulations (WRLES) of a high-lift airfoil, based on high-order flux reconstruction (FR) commercial software Dimaxer, which runs on consumer level GPUs. A series of independence tests are conducted, includ
Externí odkaz:
http://arxiv.org/abs/2411.05686
Deep learning methods have significantly advanced medical image segmentation, yet their success hinges on large volumes of manually annotated data, which require specialized expertise for accurate labeling. Additionally, these methods often demand su
Externí odkaz:
http://arxiv.org/abs/2409.00884
Semi-supervised learning (SSL) has achieved notable progress in medical image segmentation. To achieve effective SSL, a model needs to be able to efficiently learn from limited labeled data and effectively exploiting knowledge from abundant unlabeled
Externí odkaz:
http://arxiv.org/abs/2407.05416
One-shot detection of anatomical landmarks is gaining significant attention for its efficiency in using minimal labeled data to produce promising results. However, the success of current methods heavily relies on the employment of extensive unlabeled
Externí odkaz:
http://arxiv.org/abs/2407.05412
Bent functions are maximally nonlinear Boolean functions with an even number of variables, which include a subclass of functions, the so-called hyper-bent functions whose properties are stronger than bent functions and a complete classification of hy
Externí odkaz:
http://arxiv.org/abs/2407.01946
Autor:
Liu, Keli, Ruan, Feng
A simple and intuitive method for feature selection consists of choosing the feature subset that maximizes a nonparametric measure of dependence between the response and the features. A popular proposal from the literature uses the Hilbert-Schmidt In
Externí odkaz:
http://arxiv.org/abs/2406.06903
Autor:
Cai, Ruichu, Huang, Siyang, Qiao, Jie, Chen, Wei, Zeng, Yan, Zhang, Keli, Sun, Fuchun, Yu, Yang, Hao, Zhifeng
As a key component to intuitive cognition and reasoning solutions in human intelligence, causal knowledge provides great potential for reinforcement learning (RL) agents' interpretability towards decision-making by helping reduce the searching space.
Externí odkaz:
http://arxiv.org/abs/2402.04869
The ubiquitous missing values cause the multivariate time series data to be partially observed, destroying the integrity of time series and hindering the effective time series data analysis. Recently deep learning imputation methods have demonstrated
Externí odkaz:
http://arxiv.org/abs/2402.04059
In this paper, a permeable surface nondimensional FW-H (Ffowcs Williams-Hawkings) acoustics analogy post-processing code with convective effect and AoA (angle of attack) corrections, OpenCFD-FWH, has been eveloped. OpenCFD-FWH is now used as post pro
Externí odkaz:
http://arxiv.org/abs/2312.16263