January 2019 - Bag of Tricks in Machine Learning

CV papers

January 31, 2019January 31, 2019 by admin

https://github.com/hoya012/deep_learning_object_detection

Ground truth?

January 31, 2019January 31, 2019 by admin

If you want to question whether human-produced labels for training ML models represent "ground truth," look no further than the sheer volume of misclassifications in a pair of recycling/trash bins.
— Sean J. Taylor (@seanjtaylor) January 29, 2019

To read more books and less paper

January 27, 2019January 27, 2019 by admin

CMU 10-708 PGM by Eric Xing

January 27, 2019January 27, 2019 by admin

https://www.youtube.com/channel/UCim-E6bNz7lUyKZwhgN6S1A/videos

How to Structuring Python Project

January 27, 2019January 27, 2019 by admin

https://docs.python-guide.org/writing/structure/

Fast ai course v3

January 26, 2019January 26, 2019 by admin

https://course.fast.ai/

Natural Questions Corpus (Google AI)

January 25, 2019January 25, 2019 by admin

https://ai.googleblog.com/2019/01/natural-questions-new-corpus-and.html

在文本分类任务中，有哪些论文中很少提及却对性能有重要影响的tricks？

January 24, 2019January 24, 2019 by admin

https://www.zhihu.com/question/265357659

把Cross Entropy梯度分布拉‘平’ – AAAI 2019 Oral

January 23, 2019January 23, 2019 by admin

AAAI 2019 Oral | 把Cross Entropy梯度分布拉‘平’，就能轻松超越Focal Loss
https://www.jiqizhixin.com/articles/2019-01-17-30

Kaggle Mercari Price Suggestion Challenge (1 place) — Pawel Jankiewicz, Konstantin Lopuhin

January 22, 2019January 22, 2019 by admin

ShujianFollow

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Julien Plu @julienplu·

Ever wanted to deploy the fast @TensorFlow models from @huggingface Transformers in TensorFlow Serving? Check out the following:
Tutorial: https://huggingface.co/blog/tf-serving
Notebook: https://tinyurl.com/y6r5ch2v

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Graham Neubig @gneubig·

If you're looking for some nice videos on cutting-edge NLP research, check out the @LTIatCMU YouTube Channel with presentations by LTI members and guest speakers! https://www.youtube.com/c/LTIatCMU

我们的中国朋友也可以观看bilibili：https://space.bilibili.com/1377044784

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Gregory Koberger @gkoberger·

Now this is patience....

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arXiv in review @arxiv_in_review·

#NeurIPS2020 Parameterized Explainer for Graph Neural Network. (arXiv:2011.04573v1 [cs\.LG] CROSS LISTED) http://arxiv.org/abs/2011.04573

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Facebook AI@facebookai·

We’re open-sourcing a new system to train computer vision models using Transformers. Data-efficient image Transformers (DeiT) is a high-performance image classification model requiring less data & computing resources to train than previous AI models. https://ai.facebook.com/blog/data-efficient-image-transformers-a-promising-new-technique-for-image-classification/

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