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

ML @ CMU

In close collaboration with the UN and local NGOs, we co-develop an interpretable predictive tool for landmine contamination to identify hazardous clusters under geographic and budget constraints, experimentally reducing false alarms and clearance time by half. Validation results in Colombia. RELand is our interpretable IRM model.

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Top 17 trending interview questions for AI Scientists

Data Science Dojo

The demand for AI scientist is projected to grow significantly in the coming years, with the U.S. AI researcher role is consistently ranked among the highest-paying jobs, attracting top talent and driving significant compensation packages. This is used for tasks like clustering, dimensionality reduction, and anomaly detection.

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Gaussian Mixture Model: A Comprehensive Guide

Pickl AI

It excels in soft clustering, handling overlapping clusters, and modelling diverse cluster shapes. Its ability to model complex, multimodal data distributions makes it invaluable for clustering , density estimation, and pattern recognition tasks. GMM handles overlapping and non-spherical clusters better than K-Means.

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Get Maximum Value from Your Visual Data

DataRobot

The value of AI these days is undeniable. AI technology is playing a massive part in the 4th industrial revolution and spread across most organizations. DataRobot Visual AI. In 2020, our team launched DataRobot Visual AI. What’s New In Visual AI. Our team worked hard to take Visual AI to the next level.

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Meet the winners of the Forecast and Final Prize Stages of the Water Supply Forecast Rodeo

DrivenData Labs

Final Stage Overall Prizes where models were rigorously evaluated with cross-validation and model reports were judged by a panel of experts. The cross-validations for all winners were reproduced by the DrivenData team. Lower is better. Unsurprisingly, the 0.10 quantile was easier to predict than the 0.90

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How IDIADA optimized its intelligent chatbot with Amazon Bedrock

AWS Machine Learning Blog

AI now plays a pivotal role in the development and evolution of the automotive sector, in which Applus+ IDIADA operates. In this post, we showcase the research process undertaken to develop a classifier for human interactions in this AI-based environment using Amazon Bedrock.

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How Amazon trains sequential ensemble models at scale with Amazon SageMaker Pipelines

AWS Machine Learning Blog

The approach uses three sequential BERTopic models to generate the final clustering in a hierarchical method. Clustering We use the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) method to form different use case clusters. Lastly, a third layer is used for some of the clusters to create sub-topics.

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