Tag: nlp

PEER: A Collaborative Language Model
abs: https://t.co/lEzymkMSph pic.twitter.com/cq28sB1zrz

— AK (@_akhaliq) August 25, 2022

Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization
abs: https://t.co/Xaspq4bZRP

model is parameter-efficient in that it outperforms the 600x larger PaLM540B on XSum, and the finetuned 200x larger GPT3175B on SAMSum pic.twitter.com/h3ZyLAMRLQ

— AK (@_akhaliq) August 23, 2022

Atlas: a not-so-large language model (11B parameters) that beats the big guys at question answering and fact checking.
The main difference is that it can retrieve facts from a corpus.
Paper: https://t.co/WlV3rbuY30 https://t.co/7bsNXfqV6l

— Yann LeCun (@ylecun) August 8, 2022

A very good paper I came across this morning by the @DeepMind researchers. For the past five years Transformers have been one of the most dominant approaches to Deep Learning problems, especially in the #NLP domain.

1/5 pic.twitter.com/XRQODHdQn3

— Bojan Tunguz (@tunguz) August 5, 2022

Masked Vision and Language Modeling for Multi-modal Representation Learning
abs: https://t.co/zpOExcblUH pic.twitter.com/siunmulnng

— AK (@_akhaliq) August 4, 2022

NLP techniques are already having a major impact on social sciences. They could potentially revolutionize many fields in the upcoming years. Thanks ⁦@DannyCEbanks⁩ for this book recommendation.https://t.co/kxVnBHXqLD pic.twitter.com/t59128tbNQ

— Bojan Tunguz (@tunguz) July 29, 2022

A @Gradio Demo for OCR-free Document Understanding Transformer on @huggingface Spaces

demo: https://t.co/o0Gdheg8O4
Get started with Gradio: https://t.co/qh8qpILE1S pic.twitter.com/hN3GWGHnAT

— AK (@_akhaliq) July 28, 2022

Check out a new hybrid approach that leverages both ML and rule-based semantic engines to build a Transformer-based semantic code completion model, which we show can improve developer efficiency. Read more ↓ https://t.co/bu6dTdPgII

— Google AI (@GoogleAI) July 26, 2022

Along with the @YouTube search team, we developed AutoChapters, an AI system that can rapidly summarise video transcripts and suggest chapter and video titles for YouTube creators. AutoChapters has helped save time for viewers and content creators alike. 4/4 pic.twitter.com/bne7mz2Oeo

— DeepMind (@DeepMind) July 14, 2022

N-Grammer: Augmenting Transformers with latent n-grams
abs: https://t.co/Rx9wzbjoHj

propose modification to the Transformer architecture by augmenting the model with n-grams that are constructed from a discrete latent representation of the text sequence pic.twitter.com/RzrRBcVGR9

— AK (@_akhaliq) July 14, 2022

Implementation of Parti, Google's pure attention-based text-to-image neural network, in Pytorch https://t.co/fAhIdq5p6v #deeplearning #machinelearning #ml #ai #neuralnetworks #datascience #pytorch

— PyTorch Best Practices (@PyTorchPractice) June 25, 2022

A quick thread on "How DALL-E 2, Imagen and Parti Architectures Differ" with breakdown into comparable modules, annotated with size 🧵#dalle2 #imagen #parti

* figures taken from corresponding papers with slight modification
* parts used for training only are greyed out pic.twitter.com/9zsIUq3toU

— Rosanne Liu (@savvyRL) June 25, 2022