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
Deep Learning and Reinforcement Learning Summer School, Toronto 2018
videos:
https://videolectures.net/DLRLsummerschool2018_toronto/
slides:
https://dlrlsummerschool.ca/speaker-slides/
videos:
https://videolectures.net/DLRLsummerschool2018_toronto/
slides:
https://dlrlsummerschool.ca/speaker-slides/
Prof. Patrick Henry Winston introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence.
https://www.newworldai.com/artificial-intelligence-complete-lectures-01-23/
https://www.newworldai.com/artificial-intelligence-complete-lectures-01-23/
New World : Artificial Intelligence
MIT Artificial Intelligence | 23 Lectures | Patrick H. Winston | 2010 - New World : Artificial Intelligence
Prof. Patrick Henry Winston introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence.
Playing Atari with Six Neurons
Cuccu et al.: https://arxiv.org/pdf/1806.01363.pdf
#artificialintelligence #machinelearning #deeplearning #neuralnetworks
Cuccu et al.: https://arxiv.org/pdf/1806.01363.pdf
#artificialintelligence #machinelearning #deeplearning #neuralnetworks
Illustrated Deep Learning cheatsheets covering Stanford's CS 230 class
Set of illustrated Deep Learning cheatsheets covering the content of Stanford's CS 230 class:
Convolutional Neural Networks: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks
Recurrent Neural Networks: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks
Tips and tricks: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-deep-learning-tips-and-tricks
Set of illustrated Deep Learning cheatsheets covering the content of Stanford's CS 230 class:
Convolutional Neural Networks: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks
Recurrent Neural Networks: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks
Tips and tricks: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-deep-learning-tips-and-tricks
stanford.edu
CS 230 - Convolutional Neural Networks Cheatsheet
Teaching page of Shervine Amidi, Graduate Student at Stanford University.
Understanding Neural Networks via Feature Visualization: A survey
Nguyen et al.: https://arxiv.org/pdf/1904.08939v1.pdf
#neuralnetworks #generatornetwork #generativemodels
Nguyen et al.: https://arxiv.org/pdf/1904.08939v1.pdf
#neuralnetworks #generatornetwork #generativemodels
Unsupervised Learning with Graph Neural Networks
By Thomas Kipf.
Slides : https://helper.ipam.ucla.edu/publications/glws4/glws4_15546.pdf
Recording: https://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/?tab=schedule
#deeplearning #neuralnetworks #unsupervisedlearning #technology
By Thomas Kipf.
Slides : https://helper.ipam.ucla.edu/publications/glws4/glws4_15546.pdf
Recording: https://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/?tab=schedule
#deeplearning #neuralnetworks #unsupervisedlearning #technology
Path-Augmented Graph Transformer Network
Chen et al.: https://arxiv.org/abs/1905.12712
#ArtificialIntelligence #DeepLearning #MachineLearning
Chen et al.: https://arxiv.org/abs/1905.12712
#ArtificialIntelligence #DeepLearning #MachineLearning