Top 100 Neuroscience Blogs And Websites For Neuroscientists in 2019

https://blog.feedspot.com/neuroscience_blogs/

@ArtificialIntelligenceArticles

The first video GAN with sparse input release by Facebook recently
Paper: https://research.fb.com/publications/deepfovea-neural-reconstruction-for-foveated-rendering-and-video-compression-using-learned-statistics-of-natural-videos/
Github: https://github.com/facebookresearch/DeepFovea
DeepFovea can decrease the number of computing resources needed for rendering by as much as 10-14x while any image differences remain imperceptible to the human eye.

An Epidemic of AI Misinformation
Gary Marcus : https://thegradient.pub/an-epidemic-of-ai-misinformation/
#ArtificialIntelligence #DeepLearning #MachineLearning

Deep Learning

[https://web.stanford.edu/class/cs230/](https://web.stanford.edu/class/cs230/)

[ Natural Language Processing ]

CS 124: From Languages to Information (LINGUIST 180, LINGUIST 280)

[https://web.stanford.edu/class/cs124/](https://web.stanford.edu/class/cs124/)

CS 224N: Natural Language Processing with Deep Learning (LINGUIST 284)

[https://web.stanford.edu/class/cs224n/](https://web.stanford.edu/class/cs224n/)

CS 224U: Natural Language Understanding (LINGUIST 188, LINGUIST 288)

[https://web.stanford.edu/class/cs224u/](https://web.stanford.edu/class/cs224u/)

CS 276: Information Retrieval and Web Search (LINGUIST 286)

[https://web.stanford.edu/class/cs](https://web.stanford.edu/class/cs224u/)276

[ Computer Vision ]
CS 131: Computer Vision: Foundations and Applications

https://[cs131.stanford.edu](https://cs131.stanford.edu/)

CS 205L: Continuous Mathematical Methods with an Emphasis on Machine Learning

[https://web.stanford.edu/class/cs205l/](https://web.stanford.edu/class/cs205l/)

CS 231N: Convolutional Neural Networks for Visual Recognition

[https://cs231n.stanford.edu/](https://cs231n.stanford.edu/)

CS 348K: Visual Computing Systems

[https://graphics.stanford.edu/courses/cs348v-18-winter/](https://graphics.stanford.edu/courses/cs348v-18-winter/)

[ Others ]

CS224W: Machine Learning with Graphs([Yong Dam Kim](https://www.facebook.com/yongdam.kim) )

[https://web.stanford.edu/class/cs224w/](https://web.stanford.edu/class/cs224w/)


CS 273B: Deep Learning in Genomics and Biomedicine (BIODS 237, BIOMEDIN 273B, GENE 236)

[https://canvas.stanford.edu/courses/51037](https://canvas.stanford.edu/courses/51037)

CS 236: Deep Generative Models

[https://deepgenerativemodels.github.io/](https://deepgenerativemodels.github.io/)

CS 228: Probabilistic Graphical Models: Principles and Techniques

[https://cs228.stanford.edu/](https://cs228.stanford.edu/)

CS 337: Al-Assisted Care (MED 277)

[https://cs337.stanford.edu/](https://cs337.stanford.edu/)

CS 229: Machine Learning (STATS 229)

[https://cs229.stanford.edu/](https://cs229.stanford.edu/)

CS 229A: Applied Machine Learning

[https://cs229a.stanford.edu](https://cs229a.stanford.edu/)

CS 234: Reinforcement Learning

https://[s234.stanford.edu](https://cs234.stanford.edu/)

CS 221: Artificial Intelligence: Principles and Techniques

[https://stanford-cs221.github.io/autumn2019/](https://stanford-cs221.github.io/autumn2019/)

Hamiltonian Graph Networks with ODE Integrators
Sanchez-Gonzalez et al.: https://arxiv.org/abs/1909.12790
#ArtificialIntelligence #Hamiltonian #GraphNetworks

Bayesian Deep Learning Benchmarks
Oxford Applied and Theoretical Machine Learning Group : https://github.com/OATML/bdl-benchmarks
#Bayesian #Benchmark #DeepLearning

We’ve completed the first fastMRI image reconstruction challenge to spur development of new AI techniques to make scans 10x faster. Congratulations to the top entrants, who’ve been invited to present at the Medical Imaging Meets NeurIPS workshop!

https://ai.facebook.com/blog/results-of-the-first-fastmri-image-reconstruction-challenge

We just released our #NeurIPS2019 Multimodal Model-Agnostic Meta-Learning (MMAML) code for learning few-shot image classification, which extends MAML to multimodal task distributions (e.g. learning from multiple datasets). The code contains #PyTorch implementations of our model and two baselines (MAML and Multi-MAML) as well as the scripts to evaluate these models to five popular few-shot learning datasets: Omniglot, Mini-ImageNet, FC100 (CIFAR100), CUB-200-2011, and FGVC-Aircraft.

Code: https://github.com/shaohua0116/MMAML-Classification

Paper: https://arxiv.org/abs/1910.13616

#NeurIPS #MachineLearning #ML #code

Mathematics for Machine Learning

Free Download Printed Book Cambridge University Press
https://mml-book.github.io/


#artificialintelligence #AI #Mathematics #calculus #linearalgebra #deeplearning #machinelearning

Single Headed Attention RNN: Stop Thinking With Your Head
Stephen Merity : https://arxiv.org/abs/1911.11423
#ArtificialIntelligence #NeuralComputing #NLP

Pre-Debate Material :
"BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop"
Maxime Chevalier-Boisvert et al.:
https://arxiv.org/abs/1810.08272v2
#AIDebate #MontrealAI #ReinforcementLearning

Datasets: 23,000 NHS Doctor Jobs Postings
Download: https://www.kaggle.com/homelesssandwich/nhs-jobs

Dive into Deep Learning (Book) by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola
Download Link: https://d2l.ai/d2l-en.pdf
Github: https://github.com/d2l-ai/d2l-en
STAT 157, UC Berkeley: https://courses.d2l.ai/berkeley-stat-157/index.html

Introducing TensorBoard.dev: a new way to share your ML experiment results
Blog by Gal Oshri : https://blog.tensorflow.org/2019/12/introducing-tensorboarddev-new-way-to.html
#ArtificialIntelligence #MachineLearning #TensorFlow

GNNExplainer: Generating Explanations for Graph Neural Networks
Ying et al. : https://arxiv.org/abs/1903.03894
Code : https://github.com/RexYing/gnn-model-explainer
#ArtificialIntelligence #Graph #NeuralNetworks

Single Sample Feature Importance: An Interpretable Algorithm for Low-Level Feature Analysis. https://arxiv.org/abs/1911.11901

AttentionGAN: Unpaired Image-to-Image Translation using Attention-Guided Generative Adver... https://arxiv.org/abs/1911.11897

Visual Physics: Discovering Physical Laws from Videos. https://arxiv.org/abs/1911.11893

What's Hidden in a Randomly Weighted Neural Network? by Ali Farhadi, Mohammad Rastegari , Vivek Ramanujan, Mitchell Wortsman, Aniruddha Kembhavi,
Ramanujan et al.: https://arxiv.org/abs/1911.13299
#Artificialintelligence #DeepLearning #MachineLearning join https://t.iss.one/ArtificialIntelligenceArticles