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81 results

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Page 1
A topical VAEGAN-IHMM approach for automatic story segmentation.
Yu J, Peng H, Wang G, Shi N. Yu J, et al. Math Biosci Eng. 2024 Jul 16;21(7):6608-6630. doi: 10.3934/mbe.2024289. Math Biosci Eng. 2024. PMID: 39176411 Free article.
To better learn topical domain representation, we used a topical classifier to supervise the training process of VAEGAN. Based on the learned feature, a segmentor splits the document into shorter ones with different topics. ...Experimental results on the TDT2 corpus demons …
To better learn topical domain representation, we used a topical classifier to supervise the training process of VAEGAN. Based on the …
VAE-WACGAN: An Improved Data Augmentation Method Based on VAEGAN for Intrusion Detection.
Tian W, Shen Y, Guo N, Yuan J, Yang Y. Tian W, et al. Sensors (Basel). 2024 Sep 18;24(18):6035. doi: 10.3390/s24186035. Sensors (Basel). 2024. PMID: 39338780 Free PMC article.
This model extends the Variational Autoencoder Generative Adversarial Network (VAEGAN) by integrating key features from the Auxiliary Classifier Generative Adversarial Network (ACGAN) and the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). ...
This model extends the Variational Autoencoder Generative Adversarial Network (VAEGAN) by integrating key features from the Auxiliary …
Synthetic augmentation for semantic segmentation of class imbalanced biomedical images: A data pair generative adversarial network approach.
Chai L, Wang Z, Chen J, Zhang G, Alsaadi FE, Alsaadi FE, Liu Q. Chai L, et al. Comput Biol Med. 2022 Nov;150:105985. doi: 10.1016/j.compbiomed.2022.105985. Epub 2022 Sep 6. Comput Biol Med. 2022. PMID: 36137319
Subsequently, to alleviate the influence from the imbalance between lesions and non-lesions areas in biomedical segmentation data sets, we divide the DPGAN into three stages, namely, background stage, mask stage and advanced stage, with each stage deploying a VAEGAN. In su …
Subsequently, to alleviate the influence from the imbalance between lesions and non-lesions areas in biomedical segmentation data sets, we d …
Fairness identification of large language models in recommendation.
Liu W, Liu B, Qin J, Zhang X, Huang W, Wang Y. Liu W, et al. Sci Rep. 2025 Feb 14;15(1):5516. doi: 10.1038/s41598-025-89965-3. Sci Rep. 2025. PMID: 39953243 Free PMC article.
For instance, the fairness-to-utility ratio for gender-based groups shows that VAEgan's results are 6.0159 and 5.0658, while ChatGLM's results achieve 30.9289 and 50.4312, respectively. ...
For instance, the fairness-to-utility ratio for gender-based groups shows that VAEgan's results are 6.0159 and 5.0658, while ChatGLM' …
Profiling DNA damage in 3D Histology Samples.
Peñas KED, Haeusler R, Feng S, Magidson V, Dmitrieva M, Wink D, Lockett S, Kinders R, Rittscher J. Peñas KED, et al. Med Opt Imaging Virtual Microsc Image Anal (2022). 2022 Sep 15:84-93. doi: 10.1007/978-3-031-16961-8_9. Online ahead of print. Med Opt Imaging Virtual Microsc Image Anal (2022). 2022. PMID: 39899002 Free PMC article.
Glaucoma affects steady state VEP contrast thresholds before psychophysics.
Vaegan, Rahman AM, Sanderson GF. Vaegan, et al. Optom Vis Sci. 2008 Jul;85(7):547-58. doi: 10.1097/OPX.0b013e31817dba51. Optom Vis Sci. 2008. PMID: 18594348 Clinical Trial.
We wanted to confirm, with stricter controls, Vaegan and Hollow's report that contrast thresholds of steady state visual evoked potentials (ssVEPs) to a stimulus resembling the central field of the FDT test was more sensitive to glaucoma than the subjective threshold to th …
We wanted to confirm, with stricter controls, Vaegan and Hollow's report that contrast thresholds of steady state visual evoked poten …
81 results