Image 2_Integrative prognostic modeling for stage III lung adenosquamous carcinoma post-tumor resection: machine learning insights and web-based implementation.png حسب Min Liang (363007)

Image 1_Integrative prognostic modeling for stage III lung adenosquamous carcinoma post-tumor resection: machine learning insights and web-based implementation.png حسب Min Liang (363007)

Data_Sheet_1_Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.pdf حسب Mikhail Ya Yadgarov (18543769)

Temperature measurement at the wire tip during TSE MRI using the standard bases (“Channel coordinate”), low-SAR bases and safe-mode PTX MRI with channel settings determined in the... حسب Maryam Arianpouya (12299222)

Image 2_Enhancing COVID-19 classification of X-ray images with hybrid deep transfer learning models.jpg حسب Maliki Moustapha (22419664)

"…Our methodology involves three different experiments: manual hyperparameter selection, k-fold retraining based on performance metrics, and the implementation of a genetic algorithm for hyperparameter optimization. …"

Image 1_Enhancing COVID-19 classification of X-ray images with hybrid deep transfer learning models.jpg حسب Maliki Moustapha (22419664)

"…Our methodology involves three different experiments: manual hyperparameter selection, k-fold retraining based on performance metrics, and the implementation of a genetic algorithm for hyperparameter optimization. …"

On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization حسب Jiancong Xiao (22265935)

"…However, we argue that the predominant approach for aligning LLMs with human preferences through a reward model—reinforcement learning from human feedback (RLHF)—suffers from an inherent algorithmic bias due to its Kullback–Leibler-based regularization in optimization. …"

Computation time as a function of the sample size on the chain graph dataset. حسب Tomokaze Shiratori (9635271)

الموضوعات:

Log-likelihood as a function of the number of selected edges on the chain graph dataset (black dashed line: The true number of edges). حسب Tomokaze Shiratori (9635271)

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Computation time as a function of the sample size on the random graph dataset. حسب Tomokaze Shiratori (9635271)

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F1 score of edges selected through cross-validation on the chain graph dataset. حسب Tomokaze Shiratori (9635271)

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Log-likelihood as a function of the number of selected edges on the random graph dataset (black dashed line: The true number of edges). حسب Tomokaze Shiratori (9635271)

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Examples of ground-truth graph structures with (<i>p</i>, <i>n</i>_≠0) = (10, 10). حسب Tomokaze Shiratori (9635271)

الموضوعات:

Number of edges selected through cross-validation on the chain graph dataset. حسب Tomokaze Shiratori (9635271)

الموضوعات:

Number of edges selected through cross-validation on the random graph dataset. حسب Tomokaze Shiratori (9635271)

الموضوعات:

Graphs of the regularization terms. حسب Tomokaze Shiratori (9635271)

الموضوعات:

F1 score of a given number of selected edges on the chain graph dataset. حسب Tomokaze Shiratori (9635271)

الموضوعات:

Computation time as a function of the number of variables on the chain graph dataset. حسب Tomokaze Shiratori (9635271)

الموضوعات:

Computation time as a function of the number of variables on the random graph dataset. حسب Tomokaze Shiratori (9635271)

الموضوعات:

F1 score of a given number of selected edges on the random graph dataset. حسب Tomokaze Shiratori (9635271)

الموضوعات: