overwrought
发表于 2025-3-28 16:11:52
,The “First Trimester (11–14 Weeks) Scan”,d images of the TLL dataset showed that the retrieving score from the first candidate guess (top-1) is . which is . higher compared to the recall score of the baseline triplet-loss which is limited to ., and with a top-5 pairing score as high as . which represents a gain of ..
Ablation
发表于 2025-3-28 20:36:29
https://doi.org/10.1007/978-981-15-8171-7encoder layers. We show that monolingual MahaBERT-based models provide rich representations as compared to sentence embeddings from multi-lingual counterparts. However, we observe that these embeddings are not generic enough and do not work well on out-of-domain social media datasets. We consider tw
湿润
发表于 2025-3-29 01:05:10
Comprehensive Gynecology and Obstetricsransformer-encoder model that takes character-level input of words and their context to achieve this. The embeddings are generated as the outputs of the model. The model is then trained to minimize triplet loss, which ensures that spell variants of a word are embedded close to the word, and that unr
Hot-Flash
发表于 2025-3-29 06:49:20
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漂亮才会豪华
发表于 2025-3-29 09:29:52
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种族被根除
发表于 2025-3-29 14:42:43
Richard K. Miller,Henry A. Thiedeesses the problem of multi-label distortion classification and ranking. A vision transformer was used for feature learning. The experiment showed that the proposed solution performed well in terms of F1 score of single distortion (77.9%) and F1 score of single and multiple distortions (69.9%). Moreo
同谋
发表于 2025-3-29 16:09:38
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conservative
发表于 2025-3-29 22:39:11
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puzzle
发表于 2025-3-29 23:53:22
Multi-stage Bias Mitigation for Individual Fairness in Algorithmic Decisionsividuals who are distinguished from other pairs in the records by data-driven similarity measures between each individual in the transformed data. Such a design identifies the bias and mitigates it at the data preprocessing stage of the machine learning pipeline to ensure individual fairness. Our me
蚊子
发表于 2025-3-30 05:29:53
A Review of Capsule Networks in Medical Image Analysis results with those of convolutional neural networks employed for the same tasks. Our findings support the use of Capsule Networks over Convolutional Neural Networks for Computer-Aided Diagnosis due to their superiority in performance but more importantly for their better interpretability and their