ByT5: Towards a token-free future with pre-trained byte-to-byte models
Pre-trained language models usually operate on the sequences of tokens, which are based on words or subword units.
Token-free models operate directly on the raw text (characters or bytes) instead. They can work with any language, are more robust to the noise, and don’t require preprocessing.
The authors use a modified mT5 architecture and show that their approach is competitive with token-level models.
Paper: https://arxiv.org/abs/2105.13626
Code: https://github.com/google-research/byt5
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-byt5
#nlp #deeplearning #transformer #pretraining
@Machine_learn
Pre-trained language models usually operate on the sequences of tokens, which are based on words or subword units.
Token-free models operate directly on the raw text (characters or bytes) instead. They can work with any language, are more robust to the noise, and don’t require preprocessing.
The authors use a modified mT5 architecture and show that their approach is competitive with token-level models.
Paper: https://arxiv.org/abs/2105.13626
Code: https://github.com/google-research/byt5
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-byt5
#nlp #deeplearning #transformer #pretraining
@Machine_learn
Reproducible_Bioinformatics_with_Python_by_Ken_Youens_Clark_Ken.pdf
6.2 MB
Reproducible Bioinformatics with Python
How to Write, Document, and Test Programs for
Biology
Ken Youens-Clark
#book #2021 #python #RL
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How to Write, Document, and Test Programs for
Biology
Ken Youens-Clark
#book #2021 #python #RL
@Machine_learn
Leordeanu_M_Unsupervised_learning_in_space_and_time_Springer_2020.pdf
6.2 MB
Unsupervised Learning in Space and Time
A Modern Approach for Computer Vision using Graph-based Techniques and Deep Neural Networks
#book #2020 #ML #DL
@Machine_learn
A Modern Approach for Computer Vision using Graph-based Techniques and Deep Neural Networks
#book #2020 #ML #DL
@Machine_learn
🔍 Contrastive Sensor Fusion
Github: https://github.com/descarteslabs/contrastive_sensor_fusion
Paper: https://arxiv.org/abs/2108.05094v1
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Github: https://github.com/descarteslabs/contrastive_sensor_fusion
Paper: https://arxiv.org/abs/2108.05094v1
@Machine_learn
📹 Internal Video Inpainting by Implicit Long-range Propagation
Github: https://github.com/Tengfei-Wang/Implicit-Internal-Video-Inpainting
Paper: https://arxiv.org/abs/2108.01912v1
4k Data: https://github.com/Tengfei-Wang/Annotated-4K-Videos
Dataset: https://paperswithcode.com/dataset/videoremoval4k
@Machine_learn
Github: https://github.com/Tengfei-Wang/Implicit-Internal-Video-Inpainting
Paper: https://arxiv.org/abs/2108.01912v1
4k Data: https://github.com/Tengfei-Wang/Annotated-4K-Videos
Dataset: https://paperswithcode.com/dataset/videoremoval4k
@Machine_learn
Tapsai2021_Book_ThaiNaturalLanguageProcessing.pdf
15 MB
Thai Natural Language
Processing
Word Segmentation, Semantic Analysis,
and Application #NLP #Book #2021
@Machine_learn
Processing
Word Segmentation, Semantic Analysis,
and Application #NLP #Book #2021
@Machine_learn
2021_Book_FormalisingNaturalLanguagesApp.pdf
33.3 MB
Formalising Natural
Languages: Applications
to Natural Language
Processing and Digital
Humanities #NLP #Book #2021
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Languages: Applications
to Natural Language
Processing and Digital
Humanities #NLP #Book #2021
@Machine_learn
Sabharwal-Agrawal2021_Book_Hands-onQuestionAnsweringSyste.pdf
4.8 MB
Hands-on Question
Answering Systems
with BERT
Applications in Neural
Networks and Natural
Language Processing #NLP #BERT #book #2021
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Answering Systems
with BERT
Applications in Neural
Networks and Natural
Language Processing #NLP #BERT #book #2021
@Machine_learn
👍1
Cicolani2021_Book_BeginningRoboticsWithRaspberry.pdf
7.3 MB
Beginning Robotics
with Raspberry Pi
and Arduino
Using Python and OpenCV
Second Edition #OpenCv #book #2021
@Machine_learn
with Raspberry Pi
and Arduino
Using Python and OpenCV
Second Edition #OpenCv #book #2021
@Machine_learn
👍1
2021_Book_SmartComputingTechniquesAndApp.pdf
33.6 MB
Smart Computing
Techniques and Applications
Proceedings of the Fourth International
Conference on Smart Computing
and Informatics, Volume 1 #book #2021
@Machine_learn
Techniques and Applications
Proceedings of the Fourth International
Conference on Smart Computing
and Informatics, Volume 1 #book #2021
@Machine_learn
🔍 TOOD: Task-aligned One-stage Object Detection
Github: https://github.com/fcjian/TOOD
Paper: https://arxiv.org/abs/2108.07755v2
Dataset: https://paperswithcode.com/dataset/coco
@Machine_learn
Github: https://github.com/fcjian/TOOD
Paper: https://arxiv.org/abs/2108.07755v2
Dataset: https://paperswithcode.com/dataset/coco
@Machine_learn
🔗 A Unified Objective for Novel Class Discovery
Github: https://github.com/DonkeyShot21/UNO
Paper: https://arxiv.org/abs/2108.08536v2
Dataset: https://paperswithcode.com/dataset/cifar-100
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Github: https://github.com/DonkeyShot21/UNO
Paper: https://arxiv.org/abs/2108.08536v2
Dataset: https://paperswithcode.com/dataset/cifar-100
@Machine_learn
💡 X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics
Github: https://github.com/yehli/xmodaler
Paper: https://arxiv.org/abs/2108.08217v1
Project: https://xmodaler.readthedocs.io/en/latest/
@Machine_learn
Github: https://github.com/yehli/xmodaler
Paper: https://arxiv.org/abs/2108.08217v1
Project: https://xmodaler.readthedocs.io/en/latest/
@Machine_learn