Neural Painters: A learned differentiable constraint for generating brushstroke paintings
By Reiichiro Nakano https://arxiv.org/abs/1904.08410
GitHub: https://github.com/reiinakano/neural-painters/tree/master/notebooks
Colab notebooks: https://colab.research.google.com/github/reiinakano/neural-painters/
By Reiichiro Nakano https://arxiv.org/abs/1904.08410
GitHub: https://github.com/reiinakano/neural-painters/tree/master/notebooks
Colab notebooks: https://colab.research.google.com/github/reiinakano/neural-painters/
4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks
Choy et al.: https://arxiv.org/abs/1904.08755
#ArtificialIntelligence #DeepLearning #NeuralNetworks
Choy et al.: https://arxiv.org/abs/1904.08755
#ArtificialIntelligence #DeepLearning #NeuralNetworks
Single Cortical Neurons as Deep Artificial Neural Networks
Beniaguev et al.: https://www.biorxiv.org/content/biorxiv/early/2019/04/18/613141.full.pdf
#DeepLearning #MachineLearning #SynapticIntegration
Beniaguev et al.: https://www.biorxiv.org/content/biorxiv/early/2019/04/18/613141.full.pdf
#DeepLearning #MachineLearning #SynapticIntegration
A List Of Top 10 Deep Learning Papers, The 2018 Edition https://www.techleer.com/articles/517-a-list-of-top-10-deep-learning-papers-the-2018-edition/
Deep Learning State of the Art (2019) by lex fridman
https://youtu.be/53YvP6gdD7U @ArtificialIntelligenceArticles
https://youtu.be/53YvP6gdD7U @ArtificialIntelligenceArticles
Harnessing Organizational Knowledge for Machine Learning
https://ai.googleblog.com/2019/03/harnessing-organizational-knowledge-for.htm
https://ai.googleblog.com/2019/03/harnessing-organizational-knowledge-for.htm
These are seven ways Google’s image analysis tools classifies uploaded images:
Faces
Objects
Labels
Web Entities
Text
Properties
Safe Search
https://www.searchenginejournal.com/google-cloud-vision-tool/304237/
Faces
Objects
Labels
Web Entities
Text
Properties
Safe Search
https://www.searchenginejournal.com/google-cloud-vision-tool/304237/
Search Engine Journal
Free Google AI Image Analysis Tool For Image Recognition
Learn how Google's Vision tool classifies images at scale and how you can use it to see the way Google interprets your images.
In medical imaging, computer aided detection (CAD) systems are available for detection of tumors and diagnosis of diseases from images acquired by imaging modalities like CT, MRI, X-ray and ultrasound. AI techniques used in computer vision such as object detection and segmentation offer unique possibilities to help radiologists make diagnosis faster and more accurately, leading to prioritization of cases, better patient care, and reduction in cost.
DNN Architecture for High Performance Prediction on Natural Videos Loses Submodule's Abil... https://arxiv.org/abs/1904.07969
A Comprehensive Study of Alzheimer's Disease Classification Using Convolutional Neural Ne... https://arxiv.org/abs/1904.07950
IAN: Combining Generative Adversarial Networks for Imaginative Face Generation. https://arxiv.org/abs/1904.07916
Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping. https://arxiv.org/abs/1904.07319
A new deep learning model developed by NVIDIA Research can turn rough doodles into photorealistic masterpieces with breathtaking ease. GauGAN converts segmentation maps into stunning lifelike images. https://bit.ly/2ugUJtv #gaugan #nvidia #deeplearning
ArtificialIntelligenceArticles
Katie Bouman TED Talk Katie Bouman is the postdoctoral fellow who led the development of the algorithm used to image a black hole. TED Talk: https://www.ted.com/talks/katie_bouman_what_does_a_black_hole_look_like
Did you know that the now famous blackhole image was processed in Python? The code is on github if you are interested to have a poke around. Remember, these are scientists not programmers so it is not all flawless code; maybe you can find some ways to improve the code? :)
https://github.com/achael/eht-imaging
https://github.com/achael/eht-imaging
GitHub
GitHub - achael/eht-imaging: Imaging, analysis, and simulation software for radio interferometry
Imaging, analysis, and simulation software for radio interferometry - achael/eht-imaging
Representer Point Selection for Explaining Deep Neural Networks by Joon Sik Kim & Chih-Kuan Yeh, #mldcmu
Why did a Deep Neural Network #DNN make a certain prediction
Learn more: https://blog.ml.cmu.edu/2019/04/19/representer-point-selection-explain-dnn/
#AI #machinelearning #deeplearning #ML
Why did a Deep Neural Network #DNN make a certain prediction
Learn more: https://blog.ml.cmu.edu/2019/04/19/representer-point-selection-explain-dnn/
#AI #machinelearning #deeplearning #ML
Machine Learning Blog | ML@CMU | Carnegie Mellon University
Representer Point Selection for Explaining Deep Neural Networks
Why did a Deep Neural Network (DNN) make a certain prediction? Although DNNs have been shown to be extremely accurate predictors in a range of domains, they are still largely black-box functions—even to the experts who train them—due to their complicated…
If you are interested in doing a PhD in Machine Learning (deep learning/deep RL) while drawing inspiration from neuroscience, consider this position https://sites.google.com/corp/view/razp/announcement
Artificial Intelligence & #Neuroscience: A Virtuous Circle https://deepmind.com/blog/ai-and-neuroscience-virtuous-circle/
Yes, We Can Now Construct Speech from Brain Waves. # #BigData #Analytics #DeepLearning #MachineLearning #DataScience #AI #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #VueJS #GoLang #Serverless #DataScientist #Linux #NeuroScience
https://www.biorxiv.org/content/10.1101/350124v2
https://www.biorxiv.org/content/10.1101/350124v2
The Machines That Will Read Your Mind: https://www.wsj.com/articles/the-machines-that-will-read-your-mind-11554476156
#AI #BigData #DataScience #MachineLearning #Neuroscience #NeuralNetworks #DeepLearning
#AI #BigData #DataScience #MachineLearning #Neuroscience #NeuralNetworks #DeepLearning