Deep Learning Gallery - a curated list of awesome deep learning projects
https://deeplearninggallery.com/
https://deeplearninggallery.com/
Deeplearninggallery
Deep Learning Gallery - a curated list of awesome deep learning projects
Showcase of the best deep learning algorithms and deep learning applications.
Deep Learning with MATLAB: Deep Learning in 11 Lines of MATLAB Code
https://nl.mathworks.com/videos/deep-learning-in-11-lines-of-matlab-code-1481229977318.html
https://nl.mathworks.com/videos/deep-learning-in-11-lines-of-matlab-code-1481229977318.html
Mathworks
Deep Learning: Deep Learning in 11 Lines of MATLAB Code
See how to use MATLAB, a simple webcam, and a deep neural network to identify objects in your surroundings. This demo uses AlexNet, a pretrained deep convolutional neural network that has been trained on over a million images.
Energy and Policy Considerations for Deep Learning in NLP.
https://drive.google.com/file/d/1v3TxkqPuzvRfiV_RVyRTTFbHl1pZq7Ab/view
https://drive.google.com/file/d/1v3TxkqPuzvRfiV_RVyRTTFbHl1pZq7Ab/view
Google Docs
acl2019.pdf
Quantum entanglement begat space-time.
Fascinating.
Those AdS spaces look a lot like Maximilian Nickel's hyperbolic space embeddings.
And those MERA tensor networks look a lot like ConvNets.
https://www.nature.com/news/the-quantum-source-of-space-time-1.18797
Fascinating.
Those AdS spaces look a lot like Maximilian Nickel's hyperbolic space embeddings.
And those MERA tensor networks look a lot like ConvNets.
https://www.nature.com/news/the-quantum-source-of-space-time-1.18797
Nature News & Comment
The quantum source of space-time
Many physicists believe that entanglement is the essence of quantum weirdness — and some now suspect that it may also be the essence of space-time geometry.
New slides: "Pretraining for Generation" at neuralgen 2019 Includes
overview of methods and new gpt-2 experiments on "pseudo-self attention"
Alexander Rush(Zack Ziegler, Luke Melas-Kyriazi, Sebastian Gehrmann)HarvardNLP / Cornell Tech
https://nlp.seas.harvard.edu/slides/Pre-training%20for%20Generation.pdf
overview of methods and new gpt-2 experiments on "pseudo-self attention"
Alexander Rush(Zack Ziegler, Luke Melas-Kyriazi, Sebastian Gehrmann)HarvardNLP / Cornell Tech
https://nlp.seas.harvard.edu/slides/Pre-training%20for%20Generation.pdf
Reliability in Reinforcement Learning
https://www.microsoft.com/en-us/research/blog/reliability-in-reinforcement-learning/?ocid=msr_blog_reliabrl_tw
https://www.microsoft.com/en-us/research/blog/reliability-in-reinforcement-learning/?ocid=msr_blog_reliabrl_tw
Encrypted Deep Learning Classification with PyTorch &
PySyft
https://blog.openmined.org/encrypted-deep-learning-classification-with-pysyft/
PySyft
https://blog.openmined.org/encrypted-deep-learning-classification-with-pysyft/
Driver Behavior Analysis Using Lane Departure Detection Under Challenging Conditions. arxiv.org/abs/1906.00093
Mesh R-CNN
Gkioxari et al.: https://arxiv.org/abs/1906.02739
#ArtificialIntelligence #DeepLearning #MachineLearning
Gkioxari et al.: https://arxiv.org/abs/1906.02739
#ArtificialIntelligence #DeepLearning #MachineLearning
arXiv.org
Mesh R-CNN
Rapid advances in 2D perception have led to systems that accurately detect objects in real-world images. However, these systems make predictions in 2D, ignoring the 3D structure of the world....
Robustness beyond Security: Computer Vision Applications
Engstrom et al.: https://gradientscience.org/robust_apps/
#artificialintelligence #computervision #security #technology
Engstrom et al.: https://gradientscience.org/robust_apps/
#artificialintelligence #computervision #security #technology
gradient science
Robustness Beyond Security: Computer Vision Applications
An off-the-shelf robust classifier can be used to perform a range of computer vision tasks beyond classification.
SuperGLUE
A new benchmark styled after GLUE with a new set of more difficult language understanding tasks, improved resources, and a new public leaderboard: https://super.gluebenchmark.com
#ArtificialIntelligence #DeepLearning #MachineLearning
A new benchmark styled after GLUE with a new set of more difficult language understanding tasks, improved resources, and a new public leaderboard: https://super.gluebenchmark.com
#ArtificialIntelligence #DeepLearning #MachineLearning
SuperGLUE Benchmark
SuperGLUE is a new benchmark styled after original GLUE benchmark with a set of more difficult language understanding tasks, improved resources, and a new public leaderboard.
What parts of ML can be designed?" Check out the colab made to introduce ML concepts
Michelle R Carney
https://colab.research.google.com/drive/16ih9JPh1FQi6_XETj2e4G4JYYI5Qe0BI#scrollTo=X8FyAQo-t2uF
Michelle R Carney
https://colab.research.google.com/drive/16ih9JPh1FQi6_XETj2e4G4JYYI5Qe0BI#scrollTo=X8FyAQo-t2uF
Google
Google Colaboratory
#Deeplearning #Automation #Scheduling
A recent success of AI & Deep learning for multi-machine/robot scheduling problems!
Arxiv link https://arxiv.org/abs/1905.12204
Three issues are particularly important in this context: quality of the resulting decisions, scalability, and transferability.
Please check out the recent research which addressed those challenges! 96% optimality, transferable only with 1% loss in performance.
A recent success of AI & Deep learning for multi-machine/robot scheduling problems!
Arxiv link https://arxiv.org/abs/1905.12204
Three issues are particularly important in this context: quality of the resulting decisions, scalability, and transferability.
Please check out the recent research which addressed those challenges! 96% optimality, transferable only with 1% loss in performance.
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks by Mohammad Rastegari
https://videolectures.net/eccv2016_rastegari_neural_networks/?q=eccv%202016
https://videolectures.net/eccv2016_rastegari_neural_networks/?q=eccv%202016
videolectures.net
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks, the filters are approximated with binary values resulting in 32x memory saving. In XNOR-Networks, both…
Google AI - Release of Handbook Tutorials on Learning Keras and OpenCV
Hi everyone. I'm happy to let people know that we (Developer Relations at Google AI) are releasing handbooks and accompany presentations/code labs for learning Keras/OpenCV. The material is written for software engineers whom want a 'straight path with no math' to learning machine learning. The handbooks and code samples are free to download (licensed under CC-BY and Apache 2.0).
https://github.com/GoogleCloudPlatform/keras-idiomatic-programmer
Hi everyone. I'm happy to let people know that we (Developer Relations at Google AI) are releasing handbooks and accompany presentations/code labs for learning Keras/OpenCV. The material is written for software engineers whom want a 'straight path with no math' to learning machine learning. The handbooks and code samples are free to download (licensed under CC-BY and Apache 2.0).
https://github.com/GoogleCloudPlatform/keras-idiomatic-programmer
GitHub
GitHub - GoogleCloudPlatform/keras-idiomatic-programmer: Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software…
Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework - GitHub - GoogleCloudPlatform/ker...
This is a PyTorch implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019) that I made. MixHop has a state-of-the-art performance on several node classification benchmarks. In addition, the approximate version is pretty scalable. Enjoy!
https://github.com/benedekrozemberczki/MixHop-and-N-GCN
https://github.com/benedekrozemberczki/MixHop-and-N-GCN
GitHub
GitHub - benedekrozemberczki/MixHop-and-N-GCN: An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via…
An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019). - benedekrozemberczki/MixHop-and-N-GCN