Causal Inference in Machine Learning

Ricardo Silva
Department of Statistical Science andCentre for Computational Statistics and Machine Learning
Imperial College

https://www.homepages.ucl.ac.uk/~ucgtrbd/talks/imperial_causality.pdf

Fooling automated surveillance cameras: adversarial patches to attack person detection
https://arxiv.org/pdf/1904.08653.pdf

Registration is still open to attend our Industrial Short Course on Deep Learning on May 16 - 17, 2019!

This 2-day course is primarily aimed at participants from industry and government agencies. The course will be given by Professor Xavier Bresson from the Nanyang Technological University (NTU) in Singapore, who is a leading researcher in the field of deep learning. Participants will learn about the theory of deep learning techniques as well as practical exercises. To register and learn more, visit the course webpage.

https://www.ipam.ucla.edu/programs/special-events-and-conferences/an-industrial-short-course-on-deep-learning-and-the-latest-ai-algorithms-2019/ #DeepLearning #Math #AI #NeuralNetworks

Brain signals translated into speech using artificial intelligence
#AI #deeplearning #recurrentneuralnetworks

A prosthetic voice decodes what the brain intends to say and generates (mostly) understandable speech, no muscle movement needed.

Many people who have lost the ability to speak communicate using technology that requires them to make tiny movements to control a cursor that selects letters or words on a screen. UK physicist Stephen Hawking, who had motor-neuron disease, was one famous example. He used a speech-generating device activated by a muscle in his cheek.

Because people who use such devices must type out words letter by letter, these devices can be very slow, producing up to ten words per minute. Natural spoken speech averages 150 words per minute. It’s the efficiency of the vocal tract that allows us to do that. So researchers decided to model the vocal system when constructing their decoder.

https://m.medicalxpress.com/news/2019-04-synthetic-speech-brain.html?fbclid=IwAR34iEZXpRhKpxQFuI6CLSTj981ugzwYRcPDUIhXx6BBsJ96p3Te58T2La0

Advices for training your Neural Network like a Recipe. !

https://karpathy.github.io/2019/04/25/recipe/

https://newsroom.gehealthcare.com/ai-helps-doctors-critical-measurement-during-pregnancy/


Automating the process of measuring the fetal brain may help get crucial data faster and easier. However, measuring the fetal brain is not an easy task and requires a significant amount of manual input from the sonographer. Today, an artificial intelligence (AI) powered tool can make this process much easier.

Exploring DNA with Deep Learning


https://blog.floydhub.com/exploring-dna-with-deep-learning/

A simple model of a neuron can be approximated by "deep network" with one hidden layer

A more detailed model of a neuron with NMDA (glutamate receptor) synapses requires SEVEN hidden layers https://www.biorxiv.org/content/10.1101/613141v1

Statistical physics of liquid brains
Pinero et al.: https://www.biorxiv.org/content/biorxiv/early/2018/11/26/478412.full.pdf
#brain #physics #technology

"If you're not having fun, you're not learning. There's a pleasure in finding things out." - Richard Feynman
#ArtificialIntelligence #DeepLearning #MachineLearning

‘Ethics Guidelines for Trustworthy AI’ Summarised
Blog by Ben Gilburt: https://towardsdatascience.com/ethics-guidelines-for-trustworthy-ai-summarised-1c86174e788b
#EuropeanUnion #AIEthics #ArtificialIntelligence #AI #Ethics

10 CUTTING EDGE RESEARCH PAPERS IN COMPUTER VISION & IMAGE GENERATION
https://www.topbots.com/most-important-ai-computer-vision-research/

A Digest of Top 35+ Research Papers on Statistics. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #JavaScript #ReactJS #GoLang #Serverless #DataScientist #Linux #Statistics

https://www.tandfonline.com/doi/full/10.1080/00031305.2019.1583913

No Time to Read Artificial Intelligence Research?
👇
Here You Have 10 Top 2018 #AI Papers Summarized For You 👌

https://www.topbots.com/most-important-ai-research-papers-2018/

Deep Learning lecture
The full deck of (600+) slides, by Gilles Louppe: https://glouppe.github.io/info8010-deep-learning/pdf/lec-all.pdf
#ArtificialIntelligence #DeepLearning #MachineLearning

Phd-level courses

here's a list of advanced courses about ML:

Advanced Introduction to ML -
https://www.cs.cmu.edu/~bapoczos/Classes/ML10715_2015Fall/index.html

https://www.youtube.com/playlist?list=PL4DwY1suLMkcu-wytRDbvBNmx57CdQ2pJ&jct=q4qVgISGxJql7TlE6eSLKa8Wwci8SA

Large Scale ML - videos

https://www.cs.toronto.edu/~rsalakhu/STA4273_2015/lectures.html

Statistical Learning Theory and Applications -
https://www.youtube.com/playlist?list=PLyGKBDfnk-iDj3FBd0Avr_dLbrU8VG73O

Regularization Methods for ML -
https://www.youtube.com/playlist?list=PLbF0BXX_6CPJ20Gf_KbLFnPWjFTvvRwCO

Statistical ML -
https://www.youtube.com/playlist?list=PLTB9VQq8WiaCBK2XrtYn5t9uuPdsNm7YE

Convex Optimization -
https://www.youtube.com/playlist?list=PLjbUi5mgii6BZBhJ9nW7eydgycyCOYeZ6

Probabilistic Graphical Models 2014 (with videos)

https://www.cs.cmu.edu/~epxing/Class/10708-14/lecture.html

Build your own robotic cat!

Blog by Alex Bate: https://www.raspberrypi.org/blog/robotic-cat-petoi-nybble/

#Arduino #ArtificialIntelligence #MachineLearning #RaspberryPi #Robotics

See arXiv’s Feedback on the Guidance on the Implementation of Plan S https://blogs.cornell.edu/arxiv/2019/02/04/arxivs-feedback-on-the-guidance-on-the-implementation-of-plan-s/ #PlanS #preprints

Computer-aided diagnosis in histopathological images of the endometrium using a convolutional neural network and attention mechanisms

https://arxiv.org/abs/1904.10626