Deep RL Bootcamp
By Pieter Abbeel, Rocky Duan, Peter Chen, Andrej Karpathy et al.: https://sites.google.com/view/deep-rl-bootcamp/lectures
#ArtificialIntelligence #DeepLearning #MachineLearning #NeuralNetworks #ReinforcementLearning

Blake Richards: Deep Learning with Ensembles of Neocortical Microcircuits


An inspiring talk by Blake Richards at the ICLR2018 emphasizing the interaction between neuroscience and machine learning. This intersection is where great things happen.

https://goo.gl/1YCjrm


https://t.iss.one/ArtificialIntelligenceArticles

Artificial Intelligence & #Neuroscience: A Virtuous Circle https://deepmind.com/blog/ai-and-neuroscience-virtuous-circle/

Machine Learning Unlocks Library of The Human Brain. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #NeuroScience


https://thetartan.org/2019/11/11/scitech/brain-thoughts

"The best way to understand the mind is to built it." Full lecture

https://youtu.be/0VH1Lim8gL8

"Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm"
Chelsea Finn and Sergey Levine : https://arxiv.org/abs/1710.11622
#MachineLearning #ArtificialIntelligence #MetaLearning #NeuralComputing

PyTorch Geometry
The PyTorch Geometry package is a geometric computer vision library for PyTorch
By Arraiy: https://github.com/arraiy/torchgeometry
#ArtificialIntelligence #ComputerVision #DeepLearning #MachineLearning #PyTorch

Poly-time universality and limitations of deep learning
Emmanuel Abbe, Colin Sandon : https://arxiv.org/abs/2001.02992
#ArtificialIntelligence #MachineLearning #InformationTheory

State of the art- Photoshop faces with hand sketches!

[https://www.profillic.com/paper/arxiv:2001.02890

(The researchers propose Deep Plastic Surgery, a novel sketch-based image editing framework to achieve both robustness on hand-drawn sketch inputs and the controllability on sketch faithfulness](https://www.profillic.com/paper/arxiv:2001.02890))

Immediate brain plasticity after one hour of brain–computer interface (BCI)
https://physoc.onlinelibrary.wiley.com/doi/10.1113/JP278118

THE AMALGAMATION OF DATA SCIENCE AND NEUROSCIENCE
https://www.analyticsinsight.net/the-amalgamation-of-data-science-and-neuroscience/

Neural Data Server: A Large-Scale Search Engine for Transfer Learning Data. https://arxiv.org/abs/2001.02799

Lifted Hybrid Variational Inference. https://arxiv.org/abs/2001.02773

Named-Entity-Recognition-NER-Papers
By Pengfei Liu, Jinlan Fu and other contributors: https://github.com/pfliu-nlp/Named-Entity-Recognition-NER-Papers
An elaborate and exhaustive paper list for Named Entity Recognition (NER), covering papers from seven top conferences (ACL / EMNLP / NAACL / Coling / ICLR / AAAI / IJCAI) and eight years (2013-2020).
#ArtificialIntelligence #DeepLearning #MachineLearning

Summary: A new convolutional neural network that utilizes MRI brain scans can forecast genetic mutations in glioma brain tumors.
Source: Osaka University
Researchers at Osaka University have developed a computer method that uses magnetic resonance imaging (MRI) and machine learning to rapidly forecast genetic mutations in glioma tumors, which occur in the brain or spine. The work may help glioma patients to receive more suitable treatment faster, giving better outcomes. The research was recently published in Scientific Reports.
https://neurosciencenews.com/genetics-brain-tumors-15451/

Detection of anaemia from retinal fundus images via deep learning https://www.nature.com/articles/s41551-019-0487-z.epdf?shared_access_token=BcBD1jh7EFkbhRR7GO7epNRgN0jAjWel9jnR3ZoTv0PM60VmCAPkIZuQsecTYZaovpLrjs4HpzIZT_r4kkYc5fNlZWatHh0N1icwpncYARKL54pqNmeNIjwcQ2eQ63-__htO1uHXwZ7GinlLrmJhhQ%3D%3D

Synthesising photo realistic images using GANs (SPADE Method). For more details refer to the original paper presented in CVPR 2019: https://arxiv.org/abs/1903.07291

Explaining the Explainer: A First Theoretical Analysis of LIME
Damien Garreau, Ulrike von Luxburg : https://arxiv.org/abs/2001.03447
#ArtificialIntelligence #DeepLearning #MachineLearning

DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection
Github: https://github.com/EndlessSora/DeeperForensics-1.0
Paper: https://arxiv.org/abs/2001.03024v1
Project Page: https://liming-jiang.com/projects/DrF1/DrF1.html
Video: https://youtu.be/b6iKqkJht38

Can an #AI deep neural network be trained to diagnose low blood sugar from the ECG signal?
https://go.nature.com/2NrqzxM

Fingerpicks are never pleasant and in some circumstances are particularly cumbersome. Taking fingerpick during the night certainly is unpleasant, especially for patients in paediatric age.

"Our innovation consisted in using artificial intelligence for automatic detecting hypoglycaemia via few ECG beats. This is relevant because ECG can be detected in any circumstance, including sleeping."
https://m.medicalxpress.com/news/2020-01-ai-glucose-ecg-fingerprick.html