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
New #deeplearning paper at the intersection of #AI #mathematics #psychology and #neuroscience: A mathematical theory of semantic development in deep neural networks: https://arxiv.org/abs/1810.10531
New preprint on state of the art #Deeplearning models of the retinal response to natural scenes; https://www.biorxiv.org/content/early/2018/06/08/340943 … The model's interior functionally matches that of the retina and it generalizes to capture decades of #neuroscience experiments on artificial stimuli
The Tracking Machine Learning challenge : Accuracy phase
Amrouche et al.:https://arxiv.org/pdf/1904.06778v1.pdf
#ArtificialIntelligence #MachineLearning #Physics @ArtificialIntelligenceArticles
Amrouche et al.:https://arxiv.org/pdf/1904.06778v1.pdf
#ArtificialIntelligence #MachineLearning #Physics @ArtificialIntelligenceArticles
Robotics: Philosophy of Mind using a Screwdriver
https://sro.sussex.ac.uk/id/eprint/19622/
https://pdfs.semanticscholar.org/344b/dbe8dc852e49ad9e2b0ed44afbece8871b2c.pdf
https://sro.sussex.ac.uk/id/eprint/19622/
https://pdfs.semanticscholar.org/344b/dbe8dc852e49ad9e2b0ed44afbece8871b2c.pdf
Dynamic Gesture Recognition by Using CNNs and Star RGB: a Temporal Information Condensation. https://arxiv.org/abs/1904.08505
Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles. https://arxiv.org/abs/1904.08494
Understanding the Effectiveness of Ultrasonic Microphone Jammer. https://arxiv.org/abs/1904.08490
Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurat... https://arxiv.org/abs/1904.08487
Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images. https://arxiv.org/abs/1904.08482
A large-scale field test on word-image classification in large historical document collec... https://arxiv.org/abs/1904.08421
Neural Painters: A learned differentiable constraint for generating brushstroke paintings. https://arxiv.org/abs/1904.08410
A Selective Overview of Deep Learning
Fan et al.: https://arxiv.org/abs/1904.05526
#ArtificialIntelligence #DeepLearning #MachineLearning
Fan et al.: https://arxiv.org/abs/1904.05526
#ArtificialIntelligence #DeepLearning #MachineLearning