January 2019 - Bag of Tricks in Machine Learning

CV papers

January 31, 2019 by admin

https://github.com/hoya012/deep_learning_object_detection

Ground truth?

January 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, 2019 by admin

CMU 10-708 PGM by Eric Xing

January 27, 2019 by admin

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

How to Structuring Python Project

January 27, 2019 by admin

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

Fast ai course v3

January 26, 2019 by admin

https://course.fast.ai/

Natural Questions Corpus (Google AI)

January 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, 2019 by admin

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

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

January 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, 2019 by admin

Shujian Follow

Software engineer @Google Travel. PhD in renewable energy from @UMassAmherst. @kaggle triple master. IR/NLP/ASR.

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François Chollet @fchollet ·

18 Mar

"It's autocomplete" is not a helpful analogy to understand LLMs. A LLM is more like a database that lets query information in natural language. You can query both knowledge, and "patterns" (associative programs seen in the training data, that can be applied to new inputs).

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Jason Wei @_jasonwei ·

13 Mar

Hot take supported by evidence: for a given NLP task, it is unwise to extrapolate performance to larger models because emergence can occur.

I manually examined all 202 tasks in BIG-Bench, and the most common category was for the scaling behavior to *unpredictably* increase.

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Cosmin Negruseri @cosminnegruseri ·

28 Feb

this slide is great, and focuses on one ranking model + postprocessing, but if your team owns an end to end system with indexing, candidate retrieval, ranking, blending oncall is even more complicated

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Jo Kristian Bergum @jobergum ·

27 Feb

Hm, a ready-to-ship e-commerce search solution with tunable hybrid ranking, auto-complete query suggestions, and query contextualized navigation. Better than any commercial vendor, but with open-source technology, seeing how the sausage is made.

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Zhiqing Sun @edwardsun0909 ·

22 Feb

How can LLMs such as GPT-3 and ChatGPT achieve greater factual accuracy without relying on an external retrieval search engine?

Our #ICLR2023 paper shows that recitation can help - like humans!

Recitation-Augmented Language Models
https://arxiv.org/abs/2210.01296

1/N

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