Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks
CBC is a defense against adversarial examples. CBC lowering the computation and execution time compared with the similar available defenses.
Link:
https://arxiv.org/abs/2001.06099
❇️ @AI_Python_EN
CBC is a defense against adversarial examples. CBC lowering the computation and execution time compared with the similar available defenses.
Link:
https://arxiv.org/abs/2001.06099
❇️ @AI_Python_EN
A novel countermeasure against fault injection attacks for AES-based cryptosystems
Is a method for rousting AES and similar cryptography algorithm that uses SBOX against fault attacks.
https://ieeexplore.ieee.org/abstract/document/7585694
❇️ @AI_Python_EN
Is a method for rousting AES and similar cryptography algorithm that uses SBOX against fault attacks.
https://ieeexplore.ieee.org/abstract/document/7585694
❇️ @AI_Python_EN
The very NeRF we all admire just got 9x faster!
https://github.com/krrish94/nerf-pytorch
Neural Radiance Fields (NeRF) paper to PyTorch
Try the (tiny-NeRF) Colab notebook at
https://colab.research.google.com/drive/1rO8xo0TemN67d4mTpakrKrLp03b9bgCX
abs: https://arxiv.org/abs/2003.08934
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
https://github.com/krrish94/nerf-pytorch
Neural Radiance Fields (NeRF) paper to PyTorch
Try the (tiny-NeRF) Colab notebook at
https://colab.research.google.com/drive/1rO8xo0TemN67d4mTpakrKrLp03b9bgCX
abs: https://arxiv.org/abs/2003.08934
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
3D Photography using Context-aware Layered Depth Inpainting
github: https://github.com/vt-vl-lab/3d-photo-inpainting
project page: https://shihmengli.github.io/3D-Photo-Inpainting/
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
github: https://github.com/vt-vl-lab/3d-photo-inpainting
project page: https://shihmengli.github.io/3D-Photo-Inpainting/
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
Transform and Tell: Entity-Aware News Image Captioning
End-to-end model which generates captions for images embedded in news articles.
Github: https://github.com/alasdairtran/transform-and-tell
Demo: https://transform-and-tell.ml/
Paper: https://arxiv.org/abs/2004.08070
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
End-to-end model which generates captions for images embedded in news articles.
Github: https://github.com/alasdairtran/transform-and-tell
Demo: https://transform-and-tell.ml/
Paper: https://arxiv.org/abs/2004.08070
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
BLEU might be Guilty but References are not Innocent - https://arxiv.org/abs/2004.06063 - We show that it is possible to calculate reliable automatic scores (even with BLEU) for high quality MT output by using a novel reference generation method.
Typical references exhibit poor diversity, concentrating around translationese language. Paraphrased references cover a wider diversity of target sentences and thus do not penalize alternative but equally accurate translations.
Releasing all reference translations gives the community a chance to revisit some of their decisions and measure quality differences for modeling techniques that produce more natural or fluent output which is penalized by standard references.
https://github.com/google/wmt19-paraphrased-references
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
Typical references exhibit poor diversity, concentrating around translationese language. Paraphrased references cover a wider diversity of target sentences and thus do not penalize alternative but equally accurate translations.
Releasing all reference translations gives the community a chance to revisit some of their decisions and measure quality differences for modeling techniques that produce more natural or fluent output which is penalized by standard references.
https://github.com/google/wmt19-paraphrased-references
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
work on COVID-19 detection from X-ray images by fine-tuning popular convolutional networks (such as ResNet, SqueezeNet, and DenseNet) here: https://arxiv.org/pdf/2004.09363.pdf
first re-labeled the publicly available COVID-19 images (collected by Joseph Paul Cohen PhD), with the help of a board-certified radiologist, and created COVID-Xray-5k dataset for binary classification of COVID-19 (we made this dataset publicly available via our Github).
We then trained multiple models on this dataset, and evaluated their sensitivity, specificity, ROC curve, AUC, and confusion matrix.
The PyTorch code for training and inference on our model are available here:
https://github.com/shervinmin/DeepCovid
Although the result looks promising, this is a first version of these models, and more experiments will be done once a larger dataset of cleanly labeled X-ray and CT images become available for COVID-19, for more concrete evaluation.
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
first re-labeled the publicly available COVID-19 images (collected by Joseph Paul Cohen PhD), with the help of a board-certified radiologist, and created COVID-Xray-5k dataset for binary classification of COVID-19 (we made this dataset publicly available via our Github).
We then trained multiple models on this dataset, and evaluated their sensitivity, specificity, ROC curve, AUC, and confusion matrix.
The PyTorch code for training and inference on our model are available here:
https://github.com/shervinmin/DeepCovid
Although the result looks promising, this is a first version of these models, and more experiments will be done once a larger dataset of cleanly labeled X-ray and CT images become available for COVID-19, for more concrete evaluation.
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
Free Read only access to e-journals backlist - content up to 2011
Free Read only access to the Brepols Complete e-book collection - content up to 2017
https://www-brepolsonline-net.proxy3.library.mcgill.ca/
Available until: 31-05-2020
Free Read only access to the Brepols Complete e-book collection - content up to 2017
https://www-brepolsonline-net.proxy3.library.mcgill.ca/
Available until: 31-05-2020
Top 5 Github Repos to Learn Data Science/ AI (or copy some code!)
1. Awesome Data Science
By: Fatih Aktürk, Hüseyin Mert & Osman Ungur, Recep Erol.
https://lnkd.in/g9VRjip
2. data-scientist-roadmap
By: MrMimic
https://lnkd.in/gBRwKVw
3. Data Science Best Resources
By: Tirthajyoti Sarkar
https://lnkd.in/ghk3yBd
4. Ds-cheatsheets
By: Favio André Vázquez
https://lnkd.in/gJHjc5X
5. DataScienceResources
By: jb
https://lnkd.in/gfn6GxN
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
1. Awesome Data Science
By: Fatih Aktürk, Hüseyin Mert & Osman Ungur, Recep Erol.
https://lnkd.in/g9VRjip
2. data-scientist-roadmap
By: MrMimic
https://lnkd.in/gBRwKVw
3. Data Science Best Resources
By: Tirthajyoti Sarkar
https://lnkd.in/ghk3yBd
4. Ds-cheatsheets
By: Favio André Vázquez
https://lnkd.in/gJHjc5X
5. DataScienceResources
By: jb
https://lnkd.in/gfn6GxN
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence
https://github.com/thunlp/LegalPapers
Paper:
https://arxiv.org/abs/2004.12158v2
https://github.com/thunlp/LegalPapers
Paper:
https://arxiv.org/abs/2004.12158v2
Jukebox: a new generative model for audio from OpenAI.
Jukebox, a model that generates music with singing in the raw audio domain.
openai.com/blog/jukebox
Article: cdn.openai.com/papers/jukebox.pdf
Examples: https://jukebox.openai.com/
Code: https://github.com/openai/jukebox
Jukebox, a model that generates music with singing in the raw audio domain.
openai.com/blog/jukebox
Article: cdn.openai.com/papers/jukebox.pdf
Examples: https://jukebox.openai.com/
Code: https://github.com/openai/jukebox
Reinforcement Learning with Augmented Data
https://mishalaskin.github.io/rad
Code: https://github.com/MishaLaskin/rad
Paper: https://arxiv.org/abs/2004.14990
https://mishalaskin.github.io/rad
Code: https://github.com/MishaLaskin/rad
Paper: https://arxiv.org/abs/2004.14990
GitHub
GitHub - MishaLaskin/rad: RAD: Reinforcement Learning with Augmented Data
RAD: Reinforcement Learning with Augmented Data . Contribute to MishaLaskin/rad development by creating an account on GitHub.
An NLU-Powered Tool to Explore COVID-19 Scientific Literature
https://ai.googleblog.com/2020/05/an-nlu-powered-tool-to-explore-covid-19.html
https://ai.googleblog.com/2020/05/an-nlu-powered-tool-to-explore-covid-19.html
research.google
An NLU-Powered Tool to Explore COVID-19 Scientific Literature
Posted by Keith Hall, Research Scientist, Natural Language Understanding, Google Research Update — 2021/05/20: We are expanding the Research Expl...
Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis ResearchAwesome Sentiment Analysis papers: https://github.com/declare-lab/awesome-sentiment-analysis
Paper: https://arxiv.org/abs/2005.00357v1
Set of Machine Learning Python plugins for GIMP
Github: https://github.com/kritiksoman/GIMP-ML
Paper: https://arxiv.org/abs/2004.13060
Demo: https://www.youtube.com/watch?v=HVwISLRow_0
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
Github: https://github.com/kritiksoman/GIMP-ML
Paper: https://arxiv.org/abs/2004.13060
Demo: https://www.youtube.com/watch?v=HVwISLRow_0
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
GitHub
GitHub - kritiksoman/GIMP-ML: AI for GNU Image Manipulation Program
AI for GNU Image Manipulation Program. Contribute to kritiksoman/GIMP-ML development by creating an account on GitHub.
How to Create a Web API using Flask and Python
https://www.i2tutorials.com/how-to-create-a-web-api-using-flask-and-python/
https://www.i2tutorials.com/how-to-create-a-web-api-using-flask-and-python/
i2tutorials
How to Create a Web API using Flask and Python | i2tutorials
Flask is a web framework written in python. It provides tools, libraries, and technologies that allow you to build a web application.
Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning
https://ai.googleblog.com/2020/05/announcing-meta-dataset-dataset-of.html
https://ai.googleblog.com/2020/05/announcing-meta-dataset-dataset-of.html
Googleblog
Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning