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Publikováno v:
Journal of Yangtze Normal University. Nov2022, Vol. 38 Issue 6, p78-84. 7p.
Autor:
Su, Qing, Ji, Shihao
Distillation-based self-supervised learning typically leads to more compressed representations due to its radical clustering process and the implementation of a sharper target distribution. To overcome this limitation and preserve more information fr
Externí odkaz:
http://arxiv.org/abs/2407.17671
Autor:
Xue Xiao, Su-Qing Yang
Publikováno v:
Journal of Hainan Medical University, Vol 27, Iss 20, Pp 55-59 (2021)
Objective: To explore Professor Yang Suqing’s experience in treating blood stasis syndrome of psoriasis vulgaris and analyze the characteristics of its prescription; Methods: Outpatient prescriptions for the treatment of psoriasis vulgaris with blo
Current self-supervised learning (SSL) methods (e.g., SimCLR, DINO, VICReg,MOCOv3) target primarily on representations at instance level and do not generalize well to dense prediction tasks, such as object detection and segmentation.Towards aligning
Externí odkaz:
http://arxiv.org/abs/2306.06203
Publikováno v:
Allergy, Asthma & Clinical Immunology, Vol 20, Iss 1, Pp 1-10 (2024)
Abstract Objective The aim of this study was to investigate the role and mechanisms of miR-155 in chronic spontaneous urticaria (CSU). Methods The expression level of miR-155 in the skin tissues of patients with CSU and experimental rats were detecte
Externí odkaz:
https://doaj.org/article/08c546383dd44caabd6ec83517cce152
Publikováno v:
Heliyon, Vol 10, Iss 15, Pp e35091- (2024)
The effect of hot isostatic pressing (HIP) on the microstructure and properties of hot dip aluminum coating cooled in a magnetic field was investigated in this study. In order to improve the microstructure and properties of magnetic dip aluminum coat
Externí odkaz:
https://doaj.org/article/59ff8b3d361e42d1ac061839fde7d786
Publikováno v:
Kybernetes, 2023, Vol. 53, Issue 4, pp. 1281-1305.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/K-04-2022-0553
Publikováno v:
Medicine; 11/3/2023, Vol. 102 Issue 44, p1-12, 12p
Can we train a hybrid discriminative-generative model within a single network? This question has recently been answered in the affirmative, introducing the field of Joint Energy-based Model (JEM), which achieves high classification accuracy and image
Externí odkaz:
http://arxiv.org/abs/2209.07959