Decrease in loss during the training process. by Muhammad Idrees (92773)

List of network connections predictive of individual SoF values, together with their β weights, in decreasing order. by Yinan Xu (12440894)

SHAP interpretation of the optimal machine learning model. by Wentao Yang (205781)

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Data from: Colony losses of stingless bees increase in agricultural areas, but decrease in forested areas by Malena Sibaja Leyton (18400983)

“…</p><p><br></p><p dir="ltr">The data are related to the scientific paper “Sibaja Leyton M. et al., (under review) Colony losses of stingless bees increase in agricultural areas, but decrease in forested areas”.…”

Comprehensive evaluation of machine-learning models in the training cohort. by Wentao Yang (205781)

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School learning model dispositions data. by David A. Katz (10056006)

“…From 2019 to 2021, reported persistent feelings of sadness/hopelessness (2019 = 36.3%, 2021 = 41.3%), seriously considering suicide (17.0-18.7%), making suicide plan(s) (14.2-16.9%), and experiencing sexual dating violence in past 12 months (6.7-7.8%) increased significantly, while alcohol use (22.5-17.1%), binge drinking (8.5-6.2%), and marijuana use (20.2-14.3%) in past 30 days, ever prescription opioid misuse (16.0-13.2%), and gun-carrying (5.5-4.5%), physical fighting (22.3-17.3%), and any sexual violence (11.3-10.4%) in past 12 months decreased significantly. Students in virtual learning jurisdictions reported relative decreases in injury during suicide attempt(s) (OR=0.68, 95%CI = 0.52-0.88), experiencing sexual violence (OR=0.78, 95%CI = 0.67-0.91), using electronic vapor products (OR=0.81, 95%CI = 0.70-0.95), using alcohol (OR=0.84, 95%CI = 0.74-0.95), and misusing prescription opioids (OR=0.82, 95%CI = 0.72-0.94). …”

Median SHAP values (calculated for CSCHF at age 5000 days) for binary features in the competing risks RSF model for different disease categories. The features are sorted in decreas... by Eric Wang (30587)

Passive sensing data. by Thierry Jean (20691795)

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Effect of active learning. by Jia Li (160557)

“…Firstly, we combine active learning with self-supervised learning to decrease the reliance on labeled data. …”

Performance comparison of the machine learning models in the training cohort (n = 29,309). by Wentao Yang (205781)

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Regression analyses of the number of all ultrasonic calls identified by the experienced researcher (A.P.; “Human”) and by the DeepSqueak machine learning tool. by Piotr Popik (292215)

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Comparison of environmental perception time results at different learning rates. by Wenhui Wang (34265)

N-back learning effects. by Ana María Triana (19819446)

Curriculum learning implementation for AbLMs. by Sarah M. Burbach (22232453)

Statistical details of data presented in Fig 16. by Piotr Popik (292215)

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Statistical details of data presented in Fig 17. by Piotr Popik (292215)

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Based on our time series data(first from the left), the visualizations of linear residual predictions under different decimation rates (next three) show that as the decimation rate... by Hua Huo (1818133)

Accuracy of multiple classifiers with a deep-learning-based feature set by Afira Aslam (21433666)

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Raw and processed data files. by Swati M. Surkar (8399427)

Data Sheet 1_Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned.pdf by Marketa Ciharova (8991782)

“…Use of multimodal data, meaning data coming from multiple sources, might contribute to machine-learning stress severity detection. …”