ℹ️ LIGAR: Lightweight General-purpose Action Recognition
Github: https://github.com/openvinotoolkit/training_extensions
Paper: https://arxiv.org/abs/2108.13153v1
Models: https://github.com/openvinotoolkit/training_extensions/tree/develop/misc
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Github: https://github.com/openvinotoolkit/training_extensions
Paper: https://arxiv.org/abs/2108.13153v1
Models: https://github.com/openvinotoolkit/training_extensions/tree/develop/misc
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📁 Open-sourcing Common Objects in 3D, a large-scale data set for 3D reconstruction
Github: https://github.com/facebookresearch/co3d
Paper: https://arxiv.org/abs/2109.00512
Facebook blog: https://github.com/openvinotoolkit/training_extensions/tree/develop/misc
Dataset: https://ai.facebook.com/datasets/co3d-downloads/
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Github: https://github.com/facebookresearch/co3d
Paper: https://arxiv.org/abs/2109.00512
Facebook blog: https://github.com/openvinotoolkit/training_extensions/tree/develop/misc
Dataset: https://ai.facebook.com/datasets/co3d-downloads/
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👨🎓 Best Resources to Learn Natural Language Processing in 2021
https://www.kdnuggets.com/2021/09/best-resources-learn-natural-language-processing-2021.html
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https://www.kdnuggets.com/2021/09/best-resources-learn-natural-language-processing-2021.html
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📹 CTRL-C: Camera calibration TRansformer with Line-Classification
Github: https://github.com/jwlee-vcl/ctrl-c
Paper: https://arxiv.org/abs/2109.02259v1
Dataset: https://drive.google.com/file/d/1opDR_j-9y4TY82ZscBb-xYAoMkTocAOp/view
@ai_machinelearning_big_data$$$
Github: https://github.com/jwlee-vcl/ctrl-c
Paper: https://arxiv.org/abs/2109.02259v1
Dataset: https://drive.google.com/file/d/1opDR_j-9y4TY82ZscBb-xYAoMkTocAOp/view
@ai_machinelearning_big_data$$$
☄️ nnFormer: Interleaved Transformer for Volumetric Segmentation
Github: https://github.com/ziniuwan/maed
Paper: https://arxiv.org/abs/2109.03201v1
Dataset: https://www.creatis.insa-lyon.fr/Challenge/acdc/
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Github: https://github.com/ziniuwan/maed
Paper: https://arxiv.org/abs/2109.03201v1
Dataset: https://www.creatis.insa-lyon.fr/Challenge/acdc/
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🔝 Reinforcement Learning Lecture Series 2021
https://deepmind.com/learning-resources/reinforcement-learning-series-2021
Video: youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm
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https://deepmind.com/learning-resources/reinforcement-learning-series-2021
Video: youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm
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🧍♂ Texformer: 3D Human Texture Estimation from a Single Image with Transformers
Github: https://github.com/xuxy09/texformer
Paper: http://arxiv.org/abs/2109.02563
Meta data: https://www.dropbox.com/s/ekxn300cuw8bw6b/meta.zip
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Github: https://github.com/xuxy09/texformer
Paper: http://arxiv.org/abs/2109.02563
Meta data: https://www.dropbox.com/s/ekxn300cuw8bw6b/meta.zip
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🔊 Textless NLP: Generating expressive speech from raw audio
Facebook Ai: https://ai.facebook.com/blog/textless-nlp-generating-expressive-speech-from-raw-audio/
Examples: https://speechbot.github.io/pgslm/
Code: https://github.com/pytorch/fairseq/tree/master/examples/textless_nlp/gslm
Paper: https://arxiv.org/abs/2102.01192
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Facebook Ai: https://ai.facebook.com/blog/textless-nlp-generating-expressive-speech-from-raw-audio/
Examples: https://speechbot.github.io/pgslm/
Code: https://github.com/pytorch/fairseq/tree/master/examples/textless_nlp/gslm
Paper: https://arxiv.org/abs/2102.01192
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✅ AliceMind: ALIbaba's Collection of Encoder-decoders from MinD (Machine IntelligeNce of Damo) Lab
Github: https://github.com/alibaba/AliceMind
Paper: https://arxiv.org/abs/2109.05687v1
Dataset: https://paperswithcode.com/dataset/glue
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Github: https://github.com/alibaba/AliceMind
Paper: https://arxiv.org/abs/2109.05687v1
Dataset: https://paperswithcode.com/dataset/glue
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🔧 Neural Networks Block Movement Pruning
Code: https://github.com/huggingface/nn_pruning
Paper: https://arxiv.org/abs/2109.04838v1
Documentation: https://github.com/huggingface/nn_pruning/blob/main/docs/HOWTO.md
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Code: https://github.com/huggingface/nn_pruning
Paper: https://arxiv.org/abs/2109.04838v1
Documentation: https://github.com/huggingface/nn_pruning/blob/main/docs/HOWTO.md
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GitHub
GitHub - huggingface/nn_pruning: Prune a model while finetuning or training.
Prune a model while finetuning or training. Contribute to huggingface/nn_pruning development by creating an account on GitHub.
👍1
📖 Physics-Based Deep Learning
Book: https://www.physicsbaseddeeplearning.org/intro.html
Github: https://github.com/thunil/Physics-Based-Deep-Learning
Deep-Flow-Prediction: https://github.com/thunil/Deep-Flow-Prediction
Paper: https://arxiv.org/abs/2109.05237v1
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Book: https://www.physicsbaseddeeplearning.org/intro.html
Github: https://github.com/thunil/Physics-Based-Deep-Learning
Deep-Flow-Prediction: https://github.com/thunil/Deep-Flow-Prediction
Paper: https://arxiv.org/abs/2109.05237v1
@ai_machinelearning_big_data
❤1
✅ AliceMind: ALIbaba's Collection of Encoder-decoders from MinD (Machine IntelligeNce of Damo) Lab
Github: https://github.com/alibaba/AliceMind
Paper: https://arxiv.org/abs/2109.05687v1
Dataset: https://paperswithcode.com/dataset/glue
Github: https://github.com/alibaba/AliceMind
Paper: https://arxiv.org/abs/2109.05687v1
Dataset: https://paperswithcode.com/dataset/glue
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🗳 Image Shape Manipulation from a Single Augmented Training Sample
Github: https://github.com/eliahuhorwitz/DeepSIM
Paper: https://arxiv.org/abs/2109.06151v1
Dataset: https://paperswithcode.com/dataset/cityscapes
Project: http://www.vision.huji.ac.il/deepsim
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Github: https://github.com/eliahuhorwitz/DeepSIM
Paper: https://arxiv.org/abs/2109.06151v1
Dataset: https://paperswithcode.com/dataset/cityscapes
Project: http://www.vision.huji.ac.il/deepsim
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DensePhrases is an extractive phrase search tool based on your natural language inputs
Github: https://github.com/princeton-nlp/DensePhrases
Paper: https://arxiv.org/abs/2109.08133v1
Dataset: https://paperswithcode.com/dataset/squad
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Github: https://github.com/princeton-nlp/DensePhrases
Paper: https://arxiv.org/abs/2109.08133v1
Dataset: https://paperswithcode.com/dataset/squad
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☄️ Uncertainty-Aware Machine Translation Evaluation
Code: https://github.com/Unbabel/COMET
Paper: https://arxiv.org/abs/2109.06352v1
Documentation: https://unbabel.github.io/COMET/html/index.html
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Code: https://github.com/Unbabel/COMET
Paper: https://arxiv.org/abs/2109.06352v1
Documentation: https://unbabel.github.io/COMET/html/index.html
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🌳 WildWood: a new Random Forest algorithm
Github: https://github.com/pyensemble/wildwood
Paper: https://arxiv.org/abs/2109.08010v1
Documentation: https://wildwood.readthedocs.io/
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Github: https://github.com/pyensemble/wildwood
Paper: https://arxiv.org/abs/2109.08010v1
Documentation: https://wildwood.readthedocs.io/
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📄 Revisiting Mask-Head Architectures for Novel Class Instance Segmentation
https://ai.googleblog.com/2021/09/revisiting-mask-head-architectures-for.html
DeepMAC model: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/deepmac.md
Paper: https://arxiv.org/abs/2104.00613
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https://ai.googleblog.com/2021/09/revisiting-mask-head-architectures-for.html
DeepMAC model: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/deepmac.md
Paper: https://arxiv.org/abs/2104.00613
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❓ Simple Entity-Centric Questions Challenge Dense Retrievers
Github: https://github.com/princeton-nlp/entityquestions
Paper: https://arxiv.org/abs/2109.08535v1
Dataset: https://paperswithcode.com/dataset/natural-questions
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Github: https://github.com/princeton-nlp/entityquestions
Paper: https://arxiv.org/abs/2109.08535v1
Dataset: https://paperswithcode.com/dataset/natural-questions
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📚 The Machine & Deep Learning Compendium Open Book
https://book.mlcompendium.com/
GitBook: https://github.com/orico/www.mlcompendium.com
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https://book.mlcompendium.com/
GitBook: https://github.com/orico/www.mlcompendium.com
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Dynamic Slimmable Network (DS-Net)
Github: https://github.com/M3DV/AlignShift
Paper: https://arxiv.org/abs/2109.10060v1
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Github: https://github.com/M3DV/AlignShift
Paper: https://arxiv.org/abs/2109.10060v1
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Language Models are Few-shot Multilingual Learners
Github: https://github.com/gentaiscool/few-shot-lm
Paper: https://arxiv.org/abs/2109.07684v1
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Github: https://github.com/gentaiscool/few-shot-lm
Paper: https://arxiv.org/abs/2109.07684v1
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