pySBD: Python Sentence Boundary Disambiguation (SBD)
is a rule-based sentence boundary detection module that works out-of-the-box.
Github: https://github.com/nipunsadvilkar/pySBD
Paper: https://arxiv.org/abs/2010.09657v1
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is a rule-based sentence boundary detection module that works out-of-the-box.
Github: https://github.com/nipunsadvilkar/pySBD
Paper: https://arxiv.org/abs/2010.09657v1
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❤1
An Example of Graph Convolutional Networks
https://blog.zakjost.com/post/gcn_citeseer/
Github: https://github.com/zjost/blog_code/tree/master/gcn_citeseer
Paper: https://arxiv.org/abs/1609.02907
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https://blog.zakjost.com/post/gcn_citeseer/
Github: https://github.com/zjost/blog_code/tree/master/gcn_citeseer
Paper: https://arxiv.org/abs/1609.02907
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Zak Jost
An Example of Graph Convolutional Networks | Zak Jost
Controlled experiments are run on the Citeseer citation graph to understand how GCNs work
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FermiNet: Quantum Physics and Chemistry from First Principles
https://deepmind.com/blog/article/FermiNet
Github: https://github.com/deepmind/ferminet
Paper: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.2.033429
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https://deepmind.com/blog/article/FermiNet
Github: https://github.com/deepmind/ferminet
Paper: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.2.033429
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TRANSFER LEARNING FOR COMPUTER VISION TUTORIAL WITH PYTORCH
https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
Code: https://github.com/pytorch/tutorials/blob/master/beginner_source/transfer_learning_tutorial.py
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https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
Code: https://github.com/pytorch/tutorials/blob/master/beginner_source/transfer_learning_tutorial.py
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Gated Linear Networks
Github: https://github.com/deepmind/deepmind-research/tree/master/gated_linear_networks
Paper: https://arxiv.org/abs/2010.12268v1
The Potential of Gated Linear Networks: https://towardsdatascience.com/the-potential-of-gated-linear-networks-for-online-learning-70ca5ea073a
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Github: https://github.com/deepmind/deepmind-research/tree/master/gated_linear_networks
Paper: https://arxiv.org/abs/2010.12268v1
The Potential of Gated Linear Networks: https://towardsdatascience.com/the-potential-of-gated-linear-networks-for-online-learning-70ca5ea073a
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📡 Athena is an open-source implementation of end-to-end speech processing engine
Github: https://github.com/athena-team/athena
Paper: https://arxiv.org/abs/2010.13991v1
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Github: https://github.com/athena-team/athena
Paper: https://arxiv.org/abs/2010.13991v1
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Dealing with Imbalanced Data in Machine Learning
https://www.kdnuggets.com/2020/10/imbalanced-data-machine-learning.html
Code: https://github.com/mwitiderrick/imbalanced-data
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https://www.kdnuggets.com/2020/10/imbalanced-data-machine-learning.html
Code: https://github.com/mwitiderrick/imbalanced-data
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KDnuggets
Dealing with Imbalanced Data in Machine Learning
This article presents tools & techniques for handling data when it's imbalanced.
Understanding of OpenSeq2Seq.
https://www.geeksforgeeks.org/understanding-of-openseq2seq/
Machine Translation: https://nvidia.github.io/OpenSeq2Seq/html/machine-translation.html
Speech Recognition: https://nvidia.github.io/OpenSeq2Seq/html/speech-recognition.html
https://www.geeksforgeeks.org/understanding-of-openseq2seq/
Machine Translation: https://nvidia.github.io/OpenSeq2Seq/html/machine-translation.html
Speech Recognition: https://nvidia.github.io/OpenSeq2Seq/html/speech-recognition.html
GeeksforGeeks
Understanding of OpenSeq2Seq - GeeksforGeeks
A Computer Science portal for geeks. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions.
🧠 Distilling Prioritized Paths For One-Shot Neural Architecture Search
Github: https://github.com/microsoft/cream
Paper: https://arxiv.org/abs/2010.15821v1
Models-Google Drive: https://drive.google.com/drive/folders/1NLGAbBF9bA1IUAxKlk2VjgRXhr6RHvRW
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Github: https://github.com/microsoft/cream
Paper: https://arxiv.org/abs/2010.15821v1
Models-Google Drive: https://drive.google.com/drive/folders/1NLGAbBF9bA1IUAxKlk2VjgRXhr6RHvRW
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Why nonlinear activation functions improve ML performance – with TensorFlow example.
https://www.machinecurve.com/index.php/2020/10/29/why-nonlinear-activation-functions-improve-ml-performance-with-tensorflow-example/
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https://www.machinecurve.com/index.php/2020/10/29/why-nonlinear-activation-functions-improve-ml-performance-with-tensorflow-example/
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End-to-end Animal Image Matting
Github: https://github.com/JizhiziLi/animal-matting
Paper: https://arxiv.org/abs/2010.16188v1
Test dataset: https://github.com/JizhiziLi/animal-matting/tree/master/demo/
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Github: https://github.com/JizhiziLi/animal-matting
Paper: https://arxiv.org/abs/2010.16188v1
Test dataset: https://github.com/JizhiziLi/animal-matting/tree/master/demo/
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🙆♂️ Gesticulator: A framework for semantically-aware speech-driven gesture generation.
https://svito-zar.github.io/gesticulator/
Code: https://github.com/Svito-zar/gesticulator
Paper: https://arxiv.org/abs/2001.09326
Video: https://www.youtube.com/watch?v=VQ8he6jjW08&feature=youtu.be
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https://svito-zar.github.io/gesticulator/
Code: https://github.com/Svito-zar/gesticulator
Paper: https://arxiv.org/abs/2001.09326
Video: https://www.youtube.com/watch?v=VQ8he6jjW08&feature=youtu.be
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CompressAI: a PyTorch library and evaluation platform for end-to-end compression research
Github: https://github.com/InterDigitalInc/CompressAI
Paper: https://arxiv.org/abs/2011.03029v1
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Github: https://github.com/InterDigitalInc/CompressAI
Paper: https://arxiv.org/abs/2011.03029v1
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RecBole. A unified, comprehensive and efficient recommendation library
https://recbole.io/
Github: https://github.com/RUCAIBox/RecBole
Paper: https://arxiv.org/abs/2011.01731
Datasets: https://github.com/RUCAIBox/RecDatasets
https://recbole.io/
Github: https://github.com/RUCAIBox/RecBole
Paper: https://arxiv.org/abs/2011.01731
Datasets: https://github.com/RUCAIBox/RecDatasets
GitHub
GitHub - RUCAIBox/RecBole: A unified, comprehensive and efficient recommendation library
A unified, comprehensive and efficient recommendation library - RUCAIBox/RecBole
Disentangling Latent Space for Unsupervised Semantic Face Editing
Github: https://github.com/max-liu-112/STGAN-WO
Paper: https://arxiv.org/abs/2011.02638
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Github: https://github.com/max-liu-112/STGAN-WO
Paper: https://arxiv.org/abs/2011.02638
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GitHub
GitHub - max-liu-112/STGAN-WO: Implementation of STGAN-WO
Implementation of STGAN-WO. Contribute to max-liu-112/STGAN-WO development by creating an account on GitHub.
📛PYTORCH CHATBOT TUTORIAL
https://pytorch.org/tutorials/beginner/chatbot_tutorial.html
Code: https://github.com/pytorch/tutorials/blob/master/beginner_source/chatbot_tutorial.py
Code for deep learning researchers to learn PyTorch:
https://github.com/yunjey/pytorch-tutorial
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https://pytorch.org/tutorials/beginner/chatbot_tutorial.html
Code: https://github.com/pytorch/tutorials/blob/master/beginner_source/chatbot_tutorial.py
Code for deep learning researchers to learn PyTorch:
https://github.com/yunjey/pytorch-tutorial
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Tequila is an Extensible Quantum Information and Learning Architecture
Github: https://github.com/aspuru-guzik-group/tequila
Paper: https://arxiv.org/abs/2011.03057v1
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Github: https://github.com/aspuru-guzik-group/tequila
Paper: https://arxiv.org/abs/2011.03057v1
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🐼 Pandas on Steroids: End to End Data Science in Python with Dask
https://www.kdnuggets.com/2020/11/pandas-steroids-dask-python-data-science.html
How to Work with BIG Datasets : https://www.kaggle.com/yuliagm/how-to-work-with-big-datasets-on-16g-ram-dask
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https://www.kdnuggets.com/2020/11/pandas-steroids-dask-python-data-science.html
How to Work with BIG Datasets : https://www.kaggle.com/yuliagm/how-to-work-with-big-datasets-on-16g-ram-dask
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KDnuggets
Pandas on Steroids: End to End Data Science in Python with Dask - KDnuggets
End to end parallelized data science from reading big data to data manipulation to visualisation to machine learning.
Deep Multimodal Fusion by Channel Exchanging
Github: https://github.com/yikaiw/CEN
Dataset: https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
Paper: https://arxiv.org/abs/2011.05005
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Github: https://github.com/yikaiw/CEN
Dataset: https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
Paper: https://arxiv.org/abs/2011.05005
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GitHub
GitHub - yikaiw/CEN: [TPAMI 2023, NeurIPS 2020] Code release for "Deep Multimodal Fusion by Channel Exchanging"
[TPAMI 2023, NeurIPS 2020] Code release for "Deep Multimodal Fusion by Channel Exchanging" - yikaiw/CEN