The Causal Inference book
Miguel A. Hernan and James M. Robins : https://cdn1.sph.harvard.edu/wp-content/uploads/sites/1268/2019/10/ci_hernanrobins_23oct19.pdf
#CausalInference

Bengio is a great researcher.
https://www.bbc.com/news/technology-50166357

Google AI Blog: Learning to Smell: Using Deep Learning to Predict the Olfactory Properties of Molecules
https://ai.googleblog.com/2019/10/learning-to-smell-using-deep-learning.html?m=1

RTFM: Generalising to Novel Environment Dynamics via Reading
Zhong et al.: https://arxiv.org/abs/1910.08210
#ArtificialIntelligence #MachineLearning #ReinforcementLearning

Open discussion of differing world views is essential for progress. Always eager to have my ideas challenged.

I invite ⁦ geoffrey hinton⁩ to help raise level of discussion via public conversation letterwiki

RT if you would like to see this happen

https://letter.wiki/conversations

High-Quality Self-Supervised Deep Image Denoising
Laine et al.: https://arxiv.org/abs/1901.10277
Code : https://github.com/NVlabs/selfsupervised-denoising
#SelfSupervisedLearning #DeepLearning #TensorFlow

Submitted to WACV 2020: Turning low-resolution pictures to super high resolution

https://www.profillic.com/paper/arxiv:1910.08761

a fully convolutional multi-stage neural network for 4× super-resolution for face images.

How to start learning AI

Do show calculus

[https://www.youtube.com/watch?v=fYyARMqiaag&list=PLF797E961509B4EB5](https://www.youtube.com/watch?v=fYyARMqiaag&list=PLF797E961509B4EB5)

calculus 1,2,3

ICYMI from BMVC 2019: human motion transfer - generation of a video
https://www.profillic.com/paper/arxiv:1910.09139
(Their GAN-based architecture, DwNet, leverages dense intermediate pose-guided representation and refinement process to warp the required subject appearance, in the form of the texture, from a source image into a desired pose.)

Bayesian Deep Learning Benchmarks
GitHub, by the Oxford Applied and Theoretical Machine Learning group : https://github.com/OATML/bdl-benchmarks
#Bayesian #DeepLearning #Benchmarks

Any technology is a double edged sword. So is the Generative adverserial networks (GANs + CNNs). Hence, the proliferation of fake videos will also become menace where, they can be used for saying/doing things that the Influential people never did to bend the facts and spead propaganda. In this astonishing talk and tech demo, Computer scientist Supasorn Suwajanakorn shows how, as a grad student, he used AI and 3D modeling to create photorealistic fake videos of people synced to audio. Learn more about both the ethical implications and the creative possibilities of this tech -- and the steps being taken to fight against its misuse.

#ArtificialIntelligence, #deeplearning #CNNS #GANS #fake #3D #Computerscience

https://www.youtube.com/watch?v=o2DDU4g0PRo

Even young children when they look at a picture, not only identify objects such as "cat," "book," "chair." but also narrate the context and probably caption them. Now, computers are getting smart enough to do that too. In this TED talk, computer vision expert Fei-Fei Li describes the state of the art — including the database of 15 million photos her team built to "teach" a computer to understand pictures — and the key insights yet to come.#alintelligence #deeplearning #datascience #machinelearning #ML #Algorithm #Python #R #professional #industry #bigdata #ai #community #workforce

https://www.youtube.com/watch?v=40riCqvRoMs

Amazing work on generative adversarial networks by Tero Karras, Samuli Laine and Timo Aila of NVIDIA. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. #education #professionals #careers #artificialintelligence #deeplearning #datascience #machinelearning #ML #Algorithm #Python #R #professional #industry #bigdata #ai #community #workforce

The research paper is available : http://stylegan.xyz/paper

Video link : https://www.youtube.com/watch?v=kSLJriaOumA

Researchers at Google deepmind work on some of the most complex and interesting challenges in AI. Their world-class research has resulted in hundreds of peer-reviewed papers, including in Nature and Science. It's a great resource to follow AI research!!

https://deepmind.com/

Now you can train an AI to swipe Tinder for you (Auto-Tinder)

https://www.marktechpost.com/2019/10/27/now-you-can-train-an-ai-to-swipe-tinder-for-you-auto-tinder/

Neuroscientists at University College London started with a simple question: Does the visual cortex represent stimuli with many different response patterns, or does it use similar patterns over and over again? The answer revealed a surprising mathematical rule at work. A power law that governs how the brain encodes sensory inputs as neural activity is tuned to keep our perceptions in balance. If the drop-off in neural responses was faster, important details would be lost. If it were slower, trivia would overwhelm us.
https://www.quantamagazine.org/a-power-law-keeps-the-brains-perceptions-balanced-20191022/

Few-Shot Unsupervised Image-to-Image Translation
paper https://arxiv.org/pdf/1905.01723.pdf
code https://github.com/NVlabs/FUNIT