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Identification of Hazardous Areas for Priority Landmine Clearance: AI for Humanitarian Mine Action

ML @ CMU

We address the challenges of landmine risk estimation by enhancing existing datasets with rich relevant features, constructing a novel, robust, and interpretable ML model that outperforms standard and new baselines, and identifying cohesive hazard clusters under geographic and budgetary constraints. Validation results in Colombia.

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Predict football punt and kickoff return yards with fat-tailed distribution using GluonTS

Flipboard

With advanced analytics derived from machine learning (ML), the NFL is creating new ways to quantify football, and to provide fans with the tools needed to increase their knowledge of the games within the game of football. We then explain the details of the ML methodology and model training procedures.

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How to Make GridSearchCV Work Smarter, Not Harder

Mlearning.ai

Figure 1: Brute Force Search It is a cross-validation technique. Figure 2: K-fold Cross Validation On the one hand, it is quite simple. Running a cross-validation model of k = 10 requires you to run 10 separate models. 2019) Data Science with Python. 2019) Applied Supervised Learning with Python.

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Announcing the Winners of Invite Only Data Challenge: OCEAN Twitter Sentiment pt. 2

Ocean Protocol

Matin split the journey, dedicating the initial 80% to training (2019–12–30 to 2022–03–28) and the final 20% to evaluation (2022–03–30 to 2022–10–21). This deployed hyperparameters tuning and cross-validation to ensure an effective and generalizable model. Describe the ML model you chose and explain why it suited this task.

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Calibration Techniques in Deep Neural Networks

Heartbeat

Label Smoothing Equation [5] In their 2019 paper “ When does label smoothing help? ”, Hinton et al. [5] logits of the final layer), which helps reduce overconfidence and subsequently reduces the network’s ECE. ” Advances in neural information processing systems 32 (2019). [6] The parameter ɑ controls the degree of smoothing.

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Identifying defense coverage schemes in NFL’s Next Gen Stats

AWS Machine Learning Blog

Through a collaboration between the Next Gen Stats team and the Amazon ML Solutions Lab , we have developed the machine learning (ML)-powered stat of coverage classification that accurately identifies the defense coverage scheme based on the player tracking data. In this post, we deep dive into the technical details of this ML model.

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Double Descent Phenomenon

Mlearning.ai

Use the cross validation technique to provide a more accurate estimate of the generalization error. Conclusion This work gives a brief overview of the double descent phenomenon, a novel concept discovered in 2019 [3]. Increase the size of training data. Figure 4: Sample-wise double descent phenomenon for linear regression model.