Accelerating cardiac cine MRI beyond compressed sensing using DL-ESPIRiT. https://arxiv.org/abs/1911.05845

TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records. https://arxiv.org/abs/1911.05843

Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. https://arxiv.org/abs/1911.05815

Computing Equilibria in Binary Networked Public Goods Games. https://arxiv.org/abs/1911.05788

China Approves Seaweed-based, Gut Bacteria-Targeting Alzheimer’s Drug - my reaction to this news is mixed - surprise, cautious optimism, & healthy dose of skepticism
https://edition.cnn.com/2019/11/03/health/china-alzheimers-drug-intl-hnk-scli/index.html

Yoshua Bengio: “AI will allow for much more personalized medicine.”

https://www.ibm.com/watson/advantage-reports/future-of-artificial-intelligence/yoshua-bengio.html

https://www.theguardian.com/technology/2018/jan/01/elon-musk-neurotechnology-human-enhancement-brain-computer-interfaces #musk #neuralink #facebook #kernel #darpa #dbs #psychosurgery
Musk created Neuralink, with the intention of connecting computers directly to human brains. He wants to do this using “neural lace” technology – implanting tiny electrodes into the brain for direct computing capabilities.
Bryan Johnson has also been testing “neural lace”. He founded Kernel, a startup to enhance human intelligence by developing brain implants linking people’s thoughts to computers.
Facebook has been hiring neuroscientists for an undisclosed project at its secretive hardware division, #Building8.
In the UK, research is ongoing. Davide Valeriani, senior research officer at University of Essex’s BCI-NE Lab, is using an electroencephalogram (EEG)-based BCI to tap into the unconscious minds of people as they make decisions

The danger of artificial intelligence isn't that it's going to rebel against us, but that it's going to do exactly what we ask it to do, says AI researcher Janelle Shane. Sharing the weird, sometimes alarming antics of AI algorithms as they try to solve human problems -- like creating new ice cream flavors or recognizing cars on the road -- Shane shows why AI doesn't yet measure up to real brains.
https://www.youtube.com/watch?v=OhCzX0iLnOc

The Illustrated GPT-2 (Visualizing Transformer Language Models)
Blog by Jay Alammar : https://jalammar.github.io/illustrated-gpt2/
#ArtificialIntelligence #NLP #UnsupervisedLearning

Conditionally Learn to Pay Attention for Sequential Visual Task

He et al.: https://arxiv.org/abs/1911.04365

#ArtificialIntelligence #DeepLearning #MachineLearning

Machine Learning:
Mathematical Theory
and Scientific Applications https://www.ams.org/journals/notices/201911/rnoti-p1813.pdf

Calculating the Backpropagation of a Network
https://medium.com/towards-artificial-intelligence/calculating-back-propagation-of-a-network-1febbcaa2b5d

CIFAR réalise une interview avec Yoshua Bengio, notre scientifique en chef à #CDLMontréal, pour leur série intitulée "The Brains Behind AI". À voir !

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CIFAR interviews #CDLMontreal’s chief scientist Yoshua Bengio for their series titled “The Brains Behind AI”. Watch it! https://www.youtube.com/watch?list=PLhzeDL-FIJK88ULiQAweocXsM_mqDwhBz&v=O6TgVE3JHfs&feature=emb_title&fbclid=IwAR3-Fj_BqR2bTgG8CgiuPxc_HEgMJb-2Ab8tLE_oVHwagDY8N4mErHd0db8&utm_source=facebook&utm_medium=lickstats&utm_campaign=yoshuabengiovideo&utm_term=&utm_content=5dd01c5e5622932605205fbf

Stacked Capsule Autoencoders
Adam R. Kosiorek, Sara Sabour, Yee Whye Teh, Geoffrey E. Hinton : https://arxiv.org/abs/1906.06818
Code : https://github.com/google-research/google-research/tree/master/stacked_capsule_autoencoders
#ArtificialIntelligence #DeepLearning #MachineLearning

Learning Keypoint Representations for Robot Manipulation
Yuke Zhu, IROS 2019 : https://ai.stanford.edu/~yukez/talks/keypoint_representations_for_interaction.pdf
#ArtificialIntelligence #DeepLearning #Robotics

Learning Neural Causal Models from Unknown Interventions
Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, Yoshua Bengio : https://arxiv.org/abs/1910.01075
#MetaLearning #MachineLearning #ArtificialIntelligence

Recurrent Independent Mechanisms
Goyal et al.: https://arxiv.org/abs/1909.10893
#MachineLearning #DeepLearning #ArtificialIntelligence

Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs
Alexia Jolicoeur-Martineau, Ioannis Mitliagkas : https://arxiv.org/abs/1910.06922
#PyTorch code: https://github.com/AlexiaJM/MaximumMarginGANs
#SupportVectorMachines #GenerativeAdversarialNetworks