内容提示: Large Margin Multi-Task Metric LearningShibin ParameswaranDepartment of Electrical and Computer EngineeringUniversity of California, San DiegoLa Jolla, CA 92093sparames@ucsd.eduKilian Q. WeinbergerDepartm
Distance Metric Learning for Large Margin Nearest Neighbor Classification,程序员大本营,技术文章内容聚合第一站。
M6-Rec surpassed all the baseline models by a large margin in terms of text quality and explainability. PETER, NTR, and Attn2Seq are commonly used as baseline models in evaluation comparisons of recommendation explanation task (Geng et al. 2022; Li et al. 2023). However, PETER outperformed ...
Gao, Y. et al. Machine learning-based automated sponge cytology for screening of oesophageal squamous cell carcinoma and adenocarcinoma of the oesophagogastric junction: a nationwide, multicohort, prospective study.Lancet Gastroenterol. Hepatol.8, 432–445 (2023). ArticlePubMedGoogle Scholar Ke, G....
For non-inferiority comparisons, a 5% absolute margin was pre-specified before the test set was inspected. The significance of the difference between the AUCs of the AI model and nnU-Net was assessed using the Delong test. The threshold to determine statistical significance is P < 0.05. ...
Considering that the corresponding data entropy consistently surpasses the pre-training entropy by a margin of 1–1.5 when the token count ranges from 200 to 250, the model excels in performance within this particular entropy range. Explain the decreased entropy range in the five-shot scenario. ...
tuning can be mainly categorized as: 1) uncertainty-based selection; 2) k-center selection (e.g., k-center greedy); and 3) submodular selection (maximized diversity). Specifically, uncertainty metrics are defined as: 1) mean entropy; 2) least confidence; 3) mean margin; 4) min margin....
This way a small margin of error for the applied regression technique is taken into account. Another strategy is to restrict the analysis to only the adaptation option with the optimally predicted value of the quality property. This strategy can be applied if the time for computing the ...
Ideally this loss function is tai-lored to the task and accurately represents the aims for the model's output. Yet learning in these models typically does not optimize performance on the true task loss, due to com-putational complexity, instead resorting to surrogate simple decomposable loss ...
“CNN+XQDA” exceeds “HOG3D+XQDA” by 55.6 % and 46.9 % in rank-1 accuracy, respectively. On MARS, the performance gap is 65.7 % and 48.5 % in rank-1 and mAP, respectively. On all the three datasets, CNN outperforms the motion features by a large margin, validating the ...