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Month: April 2019

[Fun] waiting for the model training

July 20, 2019April 27, 2019 by admin
Trying intergalactic travel before learning how to walk from ProgrammerHumor
Happens everytime from ProgrammerHumor
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A Recipe for Training Neural Networks by Andrej Karpathy

April 27, 2019 by admin

New blog post: "A Recipe for Training Neural Networks" https://t.co/5lBy4J77aS a collection of attempted advice for training neural nets with a focus on how to structure that process over time

— Andrej Karpathy (@karpathy) April 25, 2019
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Some data augmentation papers/tools

April 25, 2019 by admin

NLP:

  • Good-Enough Compositional Data Augmentation (Arxiv)
  • Easy data augmentation techniques for boosting performance on text classification tasks (Github)

Speech

  • SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition (Arxiv)

Image

  • albumentations (Github)
  • fastai (doc)
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Speech blogs/papers from top companies

April 23, 2019 by admin
  • https://developer.amazon.com/blogs/alexa/
  • https://ai.google/research/pubs/?area=SpeechProcessing
  • https://research.fb.com/publications/?cat=8
  • https://www.microsoft.com/en-us/research/blog/category/intelligence/speech-and-dialog/
  • https://machinelearning.apple.com/
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Meme time

April 22, 2019 by admin
Next Level Thinking from ProgrammerHumor
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A list of awesome mobile machine learning resources

April 22, 2019 by admin

https://github.com/fritzlabs/Awesome-Mobile-Machine-Learning/blob/master/README.md

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Reddit reply on data science hiring

April 20, 2019 by admin
Comment from discussion DoomChicken69’s comment from discussion "LinkedIn August Work Report: Employers Desperate for Data Scientists".
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AI story

April 19, 2019 by admin
[Discussion] When ML and Data Science are the death of a good company: A cautionary tale. from MachineLearning
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将门创投AI讲座

April 19, 2019 by admin

https://space.bilibili.com/209732435/video

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ICASSP 2019 Papers

April 17, 2019 by admin

ICASSP 2019 is a top tier conference in speech

https://arxiv.org/search/?searchtype=comments&query=ICASSP+2019&abstracts=show&size=200&order=-announced_date_first

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

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fchollet 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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_jasonwei 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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cosminnegruseri 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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jobergum 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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edwardsun0909 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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