When Does Label Smoothing Help? by Geoffrey Hinton , Rafael Müller, Simon Kornblith, https://arxiv.org/abs/1906.02629

Deep learning in The Brain : https://goo.gl/oqUXqV @ArtificialIntelligenceArticles

Reinforcement Learning for Market Making in a Multi-agent Dealer Market
https://arxiv.org/abs/1911.05892

It’s a no-brainer! Deep learning for brain MR images
https://medium.com/stanford-ai-for-healthcare/its-a-no-brainer-deep-learning-for-brain-mr-images-f60116397472

We have a lot to thank neurobiologists and neuroscientists for the #deeplearning revolution. A LOT!

In my lectures about #computervision you've heard me talk about the famous cat experiment by Torsten Wiesel and David Hubel.

Here they are with Stephen Kuffler (left) at the Department of Neurobiology at Harvard Medical School which was founded in 1966.

Source: Harvard Medical School.
PS: My 10 min lectures will go a bit into detail about the experiments and what they mean.
#artificiallintelligence #aiplaybook #research

If you're interested then do read their 1962 paper on "Receptive fields, binocular interaction and functional architecture in the cat's visual cortex" https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359523/?page=1

Training Agents using Upside-Down Reinforcement Learning
Srivastava et al.: https://arxiv.org/abs/1912.02877
#MachineLearning #ArtificialIntelligence #Robotics

NeurIPS 2019 Paper Awards
Neural Information Processing Systems Conference : https://medium.com/@NeurIPSConf/neurips-2019-paper-awards-807e41d0c1e
#ArtificialIntelligence #NeurIPS #NeurIPS2019

Amazing results applying transformers to symbolic function integration and differential equations solving by Guillaume Lample and François Charton from FAIR-Paris.

Succeeds in many cases where Mathematica fails.

Paper: https://arxiv.org/abs/1912.01412

Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
Grathwohl et al.: https://arxiv.org/abs/1912.03263
#ArtificialIntelligence #DeepLearning #MachineLearning

The good old days... 😊

BYTE Magazine, April 1985.

Special issue on Artificial Intelligence featuring articles by Marvin Minsky, Geoffrey E. Hinton, Patrick H. Winston, Carl Hewitt, Roger Schank, Dana E. Ballard, Jerome A. Feldman, Chris Brown and several others.

Dana, Jerry and Chris were all professors of mine at the University of Rochester where I was an undergraduate at the time.

I haven't reread the articles yet, but I'm really curious to see what "Brain-like networks" are all about. :-)

NeurIPS 2019 videos

https://slideslive.com/neurips/

World's first AI to create websites in minutes

https://fronty.com/

IT-2025
IT INNOVATION & EXCELLENCE
AWARDS 2019
Theme : Business Transformation using Cognitive and Emerging Technologies

https://www.csimumbai.org/it2025-19/

Top 10 Free Deep Learning Massive Open Online Courses

https://edgy.app/top-10-free-deep-learning-massive-open-online-courses?pfrom=tech&fp=a7&es_p=10715196

The Generative Deep Learning Book
A great book, by David Foster : https://www.mechatroniclib.com/2019/12/generative-deep-learning-teaching.html?fbclid=IwAR2pjzFjwuN8qIyEaB8DL6_DaRdAFFh92klL5qUbxKurigNsyUP9-axQmxM

"Interpretable comparison of distributions and models"
Arthur Gretton, Dougal Sutherland, Wittawat Jitkrittum
Slides :
Part 1: https://gatsby.ucl.ac.uk/~gretton/papers/neurips19_1.pdf
Part 2: https://gatsby.ucl.ac.uk/~gretton/papers/neurips19_2.pdf
Part 3: https://gatsby.ucl.ac.uk/~gretton/papers/neurips19_3.pdf
#ArtificialIntelligence #MachineLearning #NeurIPS2019