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CORD-19: A Kaggle challenge for the Natural Language Processing community.

Dataset of 29,000 scholarly article (including 13,000 with full text), about COVID-19 and related coronaviruses.
Help answers questions like "what do we know about COVID-19 risk factors?" by mining the literature.
@ArtificialIntelligenceArticles
Created by the Allen Institute for AI in partnership with the Chan-Zuckerberg Initiative, Georgetown University, Microsoft Research, and the National Library of Medicine.
https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge
https://t.me/ArtificialIntelligenceArticles
EfficientDet: Scalable and Efficient Object Detection
New scaling techniques:
Backbone: we employ the more advanced EfficientNets as our backbone networks.
BiFPN: we propose a new bi-directional feature network, named BiFPN, to enable easy and fast feature fusion. In addition to the bi-directional topology, we also propose a new fast normalized fusion that enables better fusion with negligible latency cost.
Scaling: we propose to use a single compound scaling factor to govern the network depth, width, and resolution for all backbone, feature network, and prediction networks.
Paper: https://arxiv.org/abs/1911.09070
Code: https://github.com/google/automl/tree/master/efficientdet