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A large-scale field test on word-image classification in large historical document collec...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
MR-based CT Generation (DCCC) Tensorflow Project
This repository is the implementations of the paper "MR-based Synthetic CT Generation using Deep Convolutional Neural Network Method,"
https://github.com/ChengBinJin/MRI-to-CT-DCNN-TensorFlow
This repository is the implementations of the paper "MR-based Synthetic CT Generation using Deep Convolutional Neural Network Method,"
https://github.com/ChengBinJin/MRI-to-CT-DCNN-TensorFlow
Fooling automated surveillance cameras: adversarial patches to attack person detection"
Thys et al.: https://arxiv.org/abs/1904.08653
#ArtificialIntelligence #DeepLearning #MachineLearning
Thys et al.: https://arxiv.org/abs/1904.08653
#ArtificialIntelligence #DeepLearning #MachineLearning
CS294-158 Deep Unsupervised Learning SP19
https://www.youtube.com/channel/UCf4SX8kAZM_oGcZjMREsU9w/videos?fbclid=IwAR01HPq5VLKX8aXwFbB4UWs5mZVgOOYOLUpjx_O2015rso-7U_H32hu-AKE
https://www.youtube.com/channel/UCf4SX8kAZM_oGcZjMREsU9w/videos?fbclid=IwAR01HPq5VLKX8aXwFbB4UWs5mZVgOOYOLUpjx_O2015rso-7U_H32hu-AKE
Self-Attention Generative Adversarial Networks
Zhang et al.: https://arxiv.org/abs/1805.08318
#DeepLearning #GenerativeAdversarialNetworks #MachineLearning
Zhang et al.: https://arxiv.org/abs/1805.08318
#DeepLearning #GenerativeAdversarialNetworks #MachineLearning
Functional brain network architecture supporting the learning of social networks in humans @ArtificialIntelligenceArticles
Tompson et al.: https://psyarxiv.com/r46gj/
#brainnetworks #neuroscience #socialnetworks #neuralnetworks @ArtificialIntelligenceArticles
Tompson et al.: https://psyarxiv.com/r46gj/
#brainnetworks #neuroscience #socialnetworks #neuralnetworks @ArtificialIntelligenceArticles
Tesla’s new self-driving chip is here, and this is your best look yet
https://www.theverge.com/2019/4/22/18511594/tesla-new-self-driving-chip-is-here-and-this-is-your-best-look-yet
https://www.theverge.com/2019/4/22/18511594/tesla-new-self-driving-chip-is-here-and-this-is-your-best-look-yet
Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features
Zhang et al.: https://arxiv.org/abs/1904.10014
#ArtificialIntelligence #DeepLearning #MachineLearning
Zhang et al.: https://arxiv.org/abs/1904.10014
#ArtificialIntelligence #DeepLearning #MachineLearning
arXiv.org
Linked Dynamic Graph CNN: Learning on Point Cloud via Linking...
Learning on point cloud is eagerly in demand because the point cloud is a common type of geometric data and can aid robots to understand environments robustly. However, the point cloud is sparse,...
"The anticipating brain is not a scientist: the free-energy principle from an ecological-enactive perspective"
Bruineberg et al.: https://www.ncbi.nlm.nih.gov/pubmed/30996493?dopt=Abstract
#ActiveInference #FreeEnergyPrinciple #Metastability
Bruineberg et al.: https://www.ncbi.nlm.nih.gov/pubmed/30996493?dopt=Abstract
#ActiveInference #FreeEnergyPrinciple #Metastability
www.ncbi.nlm.nih.gov
The anticipating brain is not a scientist: the free-energy principle from an ecological-enactive perspective. - PubMed - NCBI
Synthese. 2018;195(6):2417-2444. doi: 10.1007/s11229-016-1239-1. Epub 2016 Oct 21.
NVIDIA Tesla T4 GPUs are now available in Colab
Faster computations with more available memory.
Read more: https://cloud.google.com/blog/products/ai-machine-learning/nvidia-tesla-t4-gpus-now-available-in-beta
#ArtificialIntelligence #Colab #DeepLearning #MachineLearning
Faster computations with more available memory.
Read more: https://cloud.google.com/blog/products/ai-machine-learning/nvidia-tesla-t4-gpus-now-available-in-beta
#ArtificialIntelligence #Colab #DeepLearning #MachineLearning
Google Cloud Blog
NVIDIA Tesla T4 GPUs now available in beta | Google Cloud Blog
T4 GPU instances are now available publicly in beta in cloud regions around the world for machine learning, visualization and other GPU-accelerated workloads.
Introducing SuperGLUE: A New Hope Against Muppetkind
Blog by Alex Wang: https://medium.com/@wang.alex.c/introducing-superglue-a-new-hope-against-muppetkind-2779fd9dcdd5
#MachineLearning #NLP #BigData
Blog by Alex Wang: https://medium.com/@wang.alex.c/introducing-superglue-a-new-hope-against-muppetkind-2779fd9dcdd5
#MachineLearning #NLP #BigData
Medium
Introducing SuperGLUE: A New Hope Against Muppetkind
Over the past year, a machine learning models have dramatically improved scores across many language understanding tasks in NLP. ELMo…
Machine learning and complex biological data
By Chunming Xu and Scott A. Jackson
Machine learning has demonstrated potential in analyzing large, complex biological data. In practice, however, biological information is required in addition to machine learning for successful application.
https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1689-0
#artificialintelligence #machinelearning #deeplearning #biology #genomics
By Chunming Xu and Scott A. Jackson
Machine learning has demonstrated potential in analyzing large, complex biological data. In practice, however, biological information is required in addition to machine learning for successful application.
https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1689-0
#artificialintelligence #machinelearning #deeplearning #biology #genomics
BioMed Central
Machine learning and complex biological data - Genome Biology
Machine learning has demonstrated potential in analyzing large, complex biological data. In practice, however, biological information is required in addition to machine learning for successful application.