Natural Language Processing and it's Applications
https://codecampanion.blogspot.com/2019/01/natural-language-processing-and-its.html
https://codecampanion.blogspot.com/2019/01/natural-language-processing-and-its.html
Flow-Based Generative Models, Bijective Transforms and Neural Lossless Compression
https://armenag.com/2019/05/30/flow-based-generative-models-bijective-transforms-and-neural-lossless-compression/
https://armenag.com/2019/05/30/flow-based-generative-models-bijective-transforms-and-neural-lossless-compression/
Five Functions of the Brain that are Inspiring #AI Research: https://towardsdatascience.com/five-functions-of-the-brain-that-are-inspiring-ai-research-2ba482ab8e2a
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#BigData #MachineLearning #DataScience #Neuroscience #NeuralNetworks #Algorithms #DeepLearning
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#BigData #MachineLearning #DataScience #Neuroscience #NeuralNetworks #Algorithms #DeepLearning
MRI Images Created by #AI Could Help Train #DeepLearning Models. https://healthitanalytics.com/news/mri-images-created-by-ai-could-help-train-deep-learning-models #BigData #Analytics #MachineLearning #DataScience #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #VueJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #NeuroScience
Facebook #GHC19 Scholarship application is now open.
Deadline is Friday, June 29th, 5:00pm PDT.
https://www.facebook.com/careers/v2/jobs/2306282856126312/
Deadline is Friday, June 29th, 5:00pm PDT.
https://www.facebook.com/careers/v2/jobs/2306282856126312/
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Geometric Deep Learning making strides.
Videos available.
https://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/
Videos available.
https://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/
IPAM
Workshop IV: Deep Geometric Learning of Big Data and Applications - IPAM
A recent paper by Google AI researchers on scaling up CNNs in a more structured manner.
This was published in ICML 2019!
In general, the EfficientNet models achieve both higher accuracy and better efficiency over existing CNNs.
https://www.profillic.com/paper/arxiv:1905.11946
This was published in ICML 2019!
In general, the EfficientNet models achieve both higher accuracy and better efficiency over existing CNNs.
https://www.profillic.com/paper/arxiv:1905.11946
Profillic
Profillic: AI research & source code to supercharge your projects
Explore state-of-the-art in machine learning, AI, and robotics research. Browse papers, source code, models, and more by topics and authors. Connect with researchers and engineers working on related problems in machine learning, deep learning, natural language…
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities
Kohl et al.: https://arxiv.org/abs/1905.13077
#ArtificialIntelligence #DeepLearning #MachineLearning
Kohl et al.: https://arxiv.org/abs/1905.13077
#ArtificialIntelligence #DeepLearning #MachineLearning
Gaussian Differential Privacy
Dong et al.: https://arxiv.org/abs/1905.02383
#MachineLearning #Cryptography #Security #DataStructures #Algorithms
Dong et al.: https://arxiv.org/abs/1905.02383
#MachineLearning #Cryptography #Security #DataStructures #Algorithms
arXiv.org
Gaussian Differential Privacy
Differential privacy has seen remarkable success as a rigorous and practical formalization of data privacy in the past decade. This privacy definition and its divergence based relaxations,...
7 Tesla MRI of the ex vivo human brain at 100 micron resolution
Edlow et al.: https://www.biorxiv.org/content/biorxiv/early/2019/05/31/649822.full.pdf
#artificialintelligence #brain #human #technology
Edlow et al.: https://www.biorxiv.org/content/biorxiv/early/2019/05/31/649822.full.pdf
#artificialintelligence #brain #human #technology
Autonomous skill discovery with Quality-Diversity and Unsupervised Descriptors
Cully et al.: https://arxiv.org/pdf/1905.11874.pdf
#QualityDiversity #Optimization #Evolution #Robotics #DeepLearning
Cully et al.: https://arxiv.org/pdf/1905.11874.pdf
#QualityDiversity #Optimization #Evolution #Robotics #DeepLearning
Practical Full Resolution Learned Lossless Image Compression
Mentzer et al.: https://arxiv.org/abs/1811.12817
PyTorch Implementation: https://github.com/fab-jul/L3C-PyTorch
#deeplearning #machinelearning #pytorch #technology
Mentzer et al.: https://arxiv.org/abs/1811.12817
PyTorch Implementation: https://github.com/fab-jul/L3C-PyTorch
#deeplearning #machinelearning #pytorch #technology
JeVois: tiny open-source camera+ARM with ConvNet-based vision. Runs Linux, OpenCV, TF, etc. $59.
Pretty cool.
https://jevois.org/
Pretty cool.
https://jevois.org/
jevois.org
JeVois smart machine vision camera
How Renormalization Theory in Physics explains the incredible performance of Deep Neural Networks [https://towardsdatascience.com/deep-learning-explainability-hints-from-physics-2f316dc07727](https://towardsdatascience.com/deep-learning-explainability-hints-from-physics-2f316dc07727)
Medium
Deep Learning Explainability: Hints from Physics
Deep Neural Networks from a Physics Viewpoint
One of the BEST #MachineLearning Glossary by Google
It will definitely come in handy - https://developers.google.com/machine-learning/glossary/
It will definitely come in handy - https://developers.google.com/machine-learning/glossary/
Google for Developers
Machine Learning Glossary | Google for Developers
Stanford Nears All-Optical Artificial Neural Network
https://www.photonics.com/Article.aspx?AID=63684
https://www.photonics.com/Article.aspx?AID=63684
Photonics
Stanford Nears All-Optical Artificial Neural Network
Researchers have shown that it is possible to train artificial neural networks directly on an optical chip. The research demonstrates that an optical
New #deeplearning paper at the intersection of #AI #mathematics #psychology and #neuroscience: A mathematical theory of semantic development in deep neural networks: arxiv.org/abs/1810.10531 https://t.iss.one/ArtificialIntelligenceArticles
How can we guide #AI to learn the way humans do? Scientists are looking at the brain's structure to find out.
Read more in this Perspective from Science. https://science.sciencemag.org/content/363/6428/692
Read more in this Perspective from Science. https://science.sciencemag.org/content/363/6428/692