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Titlebook: Neural Information Processing; 29th International C Mohammad Tanveer,Sonali Agarwal,Adam Jatowt Conference proceedings 2023 The Editor(s) (

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樓主: STH
61#
發(fā)表于 2025-4-1 02:17:45 | 只看該作者
A Framework for?Software Defect Prediction Using Optimal Hyper-Parameters of?Deep Neural Networkr Curve (AUC). Experimental results show that the ODNN framework outperforms base DNN (BDNN) with 11.90% (accuracy), 0.26 (f-measure), and 0.13 (AUC). The statistical analysis using Wilcoxon signed-rank test and Nemenyi test show that the proposed framework is more effective than state-of-the-art models.
62#
發(fā)表于 2025-4-1 07:54:35 | 只看該作者
Anomaly Detection in?Surveillance Videos Using Transformer Based Attention Modelort range dependencies in temporal domain. This gives us a better understanding of available videos. The proposed framework is validated on real-world dataset i.e. ShanghaiTech Campus dataset which results in competitive performance than current state-of-the-art methods. The model and the code are available at ..
63#
發(fā)表于 2025-4-1 12:50:23 | 只看該作者
Automating Patient-Level Lung Cancer Diagnosis in?Different Data Regimesprovide poor results for patient-level diagnosis. In this paper, we fill this gap by introducing an end-to-end methods with a CT scan on the input and the patient-level diagnosis on the output. We consider three approaches for three different data regimes to examine how stronger and weaker supervision influences the model performance.
64#
發(fā)表于 2025-4-1 14:49:14 | 只看該作者
65#
發(fā)表于 2025-4-1 18:33:14 | 只看該作者
66#
發(fā)表于 2025-4-2 00:54:05 | 只看該作者
HYCEDIS: HYbrid Confidence Engine for?Deep Document Intelligence Systemiented Anomaly Detector, trained to faithfully estimate its confidence on its outputs without the need of host models modification. We evaluate our architecture on real-wold datasets, not only outperforming competing confidence estimators by a huge margin but also demonstrating generalization ability to out-of-distribution data.
67#
發(fā)表于 2025-4-2 02:52:44 | 只看該作者
68#
發(fā)表于 2025-4-2 07:43:44 | 只看該作者
1865-0929 mation Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022.?.The 213 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centere
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