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Copy file name to clipboardExpand all lines: README.md
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**New year resolution for 2020: read at least three paper a week and a high a high quality github repo a month!**
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## Summary of Topics
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The summary of the papers read in 2019 can be found [here on Towards Data Science](https://towardsdatascience.com/the-200-deep-learning-papers-i-read-in-2019-7fb7034f05f7?source=friends_link&sk=7628c5be39f876b2c05e43c13d0b48a3).
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The sections below records paper reading activity in chronological order.
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## What to read
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### Where to start?
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I regularly update [my blog in Toward Data Science](https://medium.com/@patrickllgc).
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-[Object detection in crowded scenes](??) ([related paper notes](topic_crowd_detection.md))
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-[Monocular Bird’s-Eye-View Semantic Segmentation for Autonomous Driving](https://towardsdatascience.com/monocular-birds-eye-view-semantic-segmentation-for-autonomous-driving-ee2f771afb59)
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-[Deep Learning in Mapping for Autonomous Driving](https://towardsdatascience.com/deep-learning-in-mapping-for-autonomous-driving-9e33ee951a44)
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-[Convolutional Neural Networks with Heterogeneous Metadata](https://towardsdatascience.com/convolutional-neural-networks-with-heterogeneous-metadata-2af9241218a9)
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-[Lifting 2D object detection to 3D in autonomous driving](https://towardsdatascience.com/geometric-reasoning-based-cuboid-generation-in-monocular-3d-object-detection-5ee2996270d1)
-[Paper Reading in 2019](https://towardsdatascience.com/the-200-deep-learning-papers-i-read-in-2019-7fb7034f05f7?source=friends_link&sk=7628c5be39f876b2c05e43c13d0b48a3)
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