admin, Author at Bag of Tricks in Machine Learning - Page 2 of 8

ACL 2019 best papers

August 4, 2019August 4, 2019 by admin

http://www.acl2019.org/EN/winners-of-acl-2019-best-paper-awards.xhtml

spaCy transformer

August 4, 2019August 4, 2019 by admin

Say hello to spacy-pytorch-transformers!

🛸 BERT, XLNet & GPT-2 in your spaCy pipeline
🤗 Based on @HuggingFace's pytorch-transformers
🎚️ Fine-tune pretrained models on your task
📦 Model packages for English & German
🚀 Token alignment, similarity & morehttps://t.co/3ftjt6pXwi
— spaCy (@spacy_io) August 2, 2019

Advances in Conversational AI by FAIR

August 4, 2019August 4, 2019 by admin

https://ai.facebook.com/blog/advances-in-conversational-ai

Affordable AI

July 25, 2019July 22, 2019 by admin

Concern: https://arxiv.org/abs/1906.02243

Good examples:

$40 to train imagenet: https://www.fast.ai/2018/08/10/fastai-diu-imagenet
Fastest end2end ASR: https://github.com/facebookresearch/wav2letter
Neural architecture search in 1.5 days on a single GPU: https://arxiv.org/abs/1806.09055

[fun] The hidden secret of ML

July 20, 2019July 20, 2019 by admin

The hidden secret of ML from datascience

[Fun] training for model

July 20, 2019July 20, 2019 by admin

Still waiting… from ProgrammerHumor

Graph net collection

July 18, 2019July 18, 2019 by admin

"Awesome Graph Classification" — A collection of graph classification methods, covering embedding, deep learning, graph kernel, and factorization papers with reference implementations https://t.co/ugpL3xSvf1
— Sebastian Raschka (@rasbt) July 16, 2019

Yoav Goldberg: The missing elements in NLP (spaCy IRL 2019)

July 13, 2019July 13, 2019 by admin

Attention mechanism

July 6, 2019July 6, 2019 by admin

Attention for interpretability:

Learning to Explain: https://arxiv.org/abs/1802.07814
Attention is not Explanation: https://arxiv.org/abs/1902.10186
Is Attention Interpretable?: https://arxiv.org/abs/1906.03731

Reviews:

NLP today

July 6, 2019July 6, 2019 by admin

NLP Today 😄 @yoavgo @spacy_io #spaCyIRL pic.twitter.com/ZXm4QeAIhF
— Christian Rehm (@Wadenschwinger) July 6, 2019

ShujianFollow

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Aran Komatsuzaki @arankomatsuzaki·

Nick Ryder (an author of GPT-3) gives a talk on GPT-3, in which he discusses a lot of things not written in the paper. W&B talks like this are highly underrated with full of great MLers. Don't forget to check other talks, too.

https://t.co/CcWWB6iO1e

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Emanuele Rossi @emaros96·

The code for our recent paper "TGN: Temporal Graph Networks" is live on Github: https://t.co/5NZPOZnPJ9 !
Paper link: https://t.co/1gUTxlC3pr
Joint work w/ @mmbronstein @frederickmonti @b_p_chamberlain @ffabffrasca @aittalam

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Amanda Askell @AmandaAskell·

The internet: We have no idea how good GPT-3 is because all of the outputs are cherry-picked.

Me, who made sure the human evaluation experiments in the paper didn't involve any cherry-picking: 😢

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Suraj Patil @psuraj28·

ONNXT5: accelerated inference for T5, built using
@onnxruntime and @huggingface transformers.
Run any of the T5 trained tasks (translation, summarization, sentiment analysis, completion, generation) at blazing speed 🔥. Built by Abel
https://t.co/m18PjNHFug

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Leo Boytsov @srchvrs·

"Deep metric learning papers have consistently claimed great advances in acc. We take closer look to see if it is actually true. We find flaws in experimental methodology of numerous metric learning papers & show that actual improvements over time have been marginal at best." https://t.co/cTOPgpmg9x

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