Reinforcement Learning Will Be 2019's Biggest Trend In Data Science
https://www.forbes.com/sites/quora/2018/10/26/reinforcement-learning-will-be-2019s-biggest-trend-in-data-science/
https://www.forbes.com/sites/quora/2018/10/26/reinforcement-learning-will-be-2019s-biggest-trend-in-data-science/
1.5 MB really feels too low to me … but maybe I should read the article first or spend more time on compressing neural language models before commenting further. 🤔 [Kids store 1.5 megabytes of information to master their native language |
https://royalsocietypublishing.org/doi/10.1098/rsos.181393
https://royalsocietypublishing.org/doi/10.1098/rsos.181393
CS224N Natural Language Processing with Deep Learning
New CS224N online hub: https://onlinehub.stanford.edu/cs224
#ArtificialIntelligence #MachineLearning #NLProc
New CS224N online hub: https://onlinehub.stanford.edu/cs224
#ArtificialIntelligence #MachineLearning #NLProc
Now anybody can GAN! Highly stable and robust architecture which requires little to no hyperparameter tuning.
https://arxiv.org/abs/1903.06048
Accompanying code is released for research purposes at https://github.com/akanimax/BMSG-GAN
https://arxiv.org/abs/1903.06048
Accompanying code is released for research purposes at https://github.com/akanimax/BMSG-GAN
The evolution of art through the lens of deep convolutional networks
“The Shape of Art History in the Eyes of the Machine”, Elgammal et al.: https://arxiv.org/pdf/1801.07729.pdf
#art #artificialintelligence #deeplearning
“The Shape of Art History in the Eyes of the Machine”, Elgammal et al.: https://arxiv.org/pdf/1801.07729.pdf
#art #artificialintelligence #deeplearning
5 NEW GENERATIVE ADVERSARIAL NETWORK (GAN) ARCHITECTURES FOR IMAGE SYNTHESIS
https://www.topbots.com/ai-research-generative-adversarial-network-images/
https://www.topbots.com/ai-research-generative-adversarial-network-images/
ThunderNet: Towards Real-time Generic Object Detection
https://arxiv.org/pdf/1903.11752v1.pdf
https://arxiv.org/pdf/1903.11752v1.pdf
Jetson Nano 😍
NVIDIA Jetson Nano enables the development of new small, low-power AI systems. It opens new worlds of embedded IoT applications, including home robots.
It's a small AI computer to create intelligent systems:https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-nano/
#ArtificialIntelligence #DeepLearning #MachineLearning #ReinforcementLearning #Robotics
NVIDIA Jetson Nano enables the development of new small, low-power AI systems. It opens new worlds of embedded IoT applications, including home robots.
It's a small AI computer to create intelligent systems:https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-nano/
#ArtificialIntelligence #DeepLearning #MachineLearning #ReinforcementLearning #Robotics
Attention in RNNs: Understanding the mechanism with a detailed example #DeepLearning
https://medium.com/datadriveninvestor/attention-in-rnns-321fbcd64f05
https://medium.com/datadriveninvestor/attention-in-rnns-321fbcd64f05
Medium
Attention in RNNs
Understanding the mechanism with a detailed example
“Nip Fake News in the bud: Introducing PreFakes” by UnFound
https://medium.com/unfound-news/nip-fake-news-in-the-bud-introducing-prefakes-69f768af2dc6
https://medium.com/unfound-news/nip-fake-news-in-the-bud-introducing-prefakes-69f768af2dc6
Reversible Adversarial Examples
Liu et al.: https://arxiv.org/abs/1811.00189
#ComputerVision #PatternRecognition #MachineLearning #MontréalAI #NeuralNetworks
Liu et al.: https://arxiv.org/abs/1811.00189
#ComputerVision #PatternRecognition #MachineLearning #MontréalAI #NeuralNetworks
Machine learning and artificial intelligence in the quantum domain"
By Vedran Dunjko, Hans J. Briegel: https://arxiv.org/abs/1709.02779
#QuantumPhysics #ArtificialIntelligence #ComputerVision #MontrealAI #PatternRecognition
By Vedran Dunjko, Hans J. Briegel: https://arxiv.org/abs/1709.02779
#QuantumPhysics #ArtificialIntelligence #ComputerVision #MontrealAI #PatternRecognition
StrokeNet: A Neural Painting Environment
Zheng et al.: https://openreview.net/forum?id=HJxwDiActX
#imagegeneration #differentiablemodel #reinforcementlearning #deeplearning #modelbased
Zheng et al.: https://openreview.net/forum?id=HJxwDiActX
#imagegeneration #differentiablemodel #reinforcementlearning #deeplearning #modelbased
Image search using multilingual texts: a cross-modal learning approach between image and text Maxime Portaz Qwant Research
https://arxiv.org/abs/1903.11299
https://arxiv.org/abs/1903.11299
arXiv.org
Image search using multilingual texts: a cross-modal learning...
Multilingual (or cross-lingual) embeddings represent several languages in a unique vector space. Using a common embedding space enables for a shared semantic between words from different...
Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening
https://medium.com/@jasonphang/deep-neural-networks-improve-radiologists-performance-in-breast-cancer-screening-565eb2bd3c9f ArXiV: https://arxiv.org/abs/1903.08297
GitHub (code & models): https://github.com/nyukat/breast_cancer_classifier
https://medium.com/@jasonphang/deep-neural-networks-improve-radiologists-performance-in-breast-cancer-screening-565eb2bd3c9f ArXiV: https://arxiv.org/abs/1903.08297
GitHub (code & models): https://github.com/nyukat/breast_cancer_classifier
DeepMindAI: Today we're releasing a large-scale extendable dataset of mathematical questions, for training (and evaluating the abilities of) neural models that can reason algebraically.
Paper: https://arxiv.org/abs/1904.01557 Code and data : https://github.com/deepmind/mathematics_dataset
Paper: https://arxiv.org/abs/1904.01557 Code and data : https://github.com/deepmind/mathematics_dataset