Remove 2016 Remove Cross Validation Remove Deep Learning
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Identification of Hazardous Areas for Priority Landmine Clearance: AI for Humanitarian Mine Action

ML @ CMU

For the Risk Modeling component, we designed a novel interpretable deep learning tabular model extending TabNet. To validate the proposed system, we simulate different scenarios in which the RELand system could be deployed in mine clearance operations using real data from Colombia. Validation results in Colombia.

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Text Classification in NLP using Cross Validation and BERT

Mlearning.ai

Deep learning models with multilayer processing architecture are now outperforming shallow or standard classification models in terms of performance [5]. Deep ensemble learning models utilise the benefits of both deep learning and ensemble learning to produce a model with improved generalisation performance.

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Calibration Techniques in Deep Neural Networks

Heartbeat

On mixup training: Improved calibration and predictive uncertainty for deep neural networks.” Measuring Calibration in Deep Learning. Eighth JPL Airborne Geoscience Workshop. 4] Szegedy, Christian, et al. Rethinking the inception architecture for computer vision. 5] Müller, Rafael, Simon Kornblith, and Geoffrey E.

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Meet the winners of Phase 2 of the PREPARE Challenge

DrivenData Labs

Solvers used 2016 demographics, economic circumstances, migration, physical limitations, self-reported health, and lifestyle behaviors to predict a composite cognitive function score in 2021. Next, for participants who had been tested in 2016, I estimated their 2021 scores by adding the predicted score difference to their 2016 scores.