Kategori: challenge

  • CommonLit Readability Prize: Resources

    CommonLit Readability Prize: Resources

    Competition Information Competition Name: CommonLit Readability Prize URL:  https://www.kaggle.com/c/commonlitreadabilityprize/overview Journals Linguistic Features for Readability Assessment – Readability assessment aims to automatically classify text by the level appropriate for learning readers. Text as Environment: A Deep Reinforcement Learning Text Readability Assessment Model Learning to Predict Readability using Diverse Linguistic Features – In this paper we consider…

  • DeepDR Diabetic Retinopathy Image Dataset (DeepDRiD) Challenge.

    DeepDR Diabetic Retinopathy Image Dataset (DeepDRiD) Challenge.

    DeepDR Diabetic Retinopathy Image Dataset (DeepDRiD) Challenge. URL: https://isbi.deepdr.org/ Aim The aim of this challenge is to evaluate algorithms for automated fundus image quality estimation and grading of diabetic retinopathy. Abstract Diabetic Retinopathy (DR) is the most prevalent cause of avoidable vision impairment, mainly affecting the working-age population in the world. Early diagnosis and timely…

  • AMLD 2020 – Transfer Learning for International Crisis Response

    URL: https://www.aicrowd.com/challenges/amld-2020-transfer-learning-for-international-crisis-response What’s the Challenge? Background Over the past 3 years, humanitarian information analysts have been using an open source platform called DEEP to facilitate collaborative, and joint analysis of unstructured data. The aim of the platform is to provide insights from years of historical and in-crisis humanitarian text data. The platform allows users to…

  • Collaborative Challenge: Detecting Drought from Space

    Collaborative Challenge: Detecting Drought from Space

    Deep Learning for Climate Adaptation: Detecting Drought from Space The challenge The dataset contains about 100,000 satellite images of Northern Kenya in 10 frequency bands, collected by the International Livestock Research Institute. Local experts (pastoralists, or nomadic herders) manually labeled the forage quality at the corresponding geolocations—specifically, the number of cows from {0, 1, 2,…