Deep Learning for Symbolic Mathematics
Guillaume Lample, François Charton : https://arxiv.org/abs/1912.01412
#ArtificialIntelligence #DeepLearning #SymbolicAI

Stacked Capsule Autoencoders by Geoffrey E． Hinton
Adam R． Kosiorek, Sara Sabour, Yee Whye Teh,
https://arxiv.org/abs/1906.06818 https://t.iss.one/ArtificialIntelligenceArticles

How To Build Your Own MuZero AI Using Python (Part 1/3)
Blog by David Foster : https://medium.com/applied-data-science/how-to-build-your-own-muzero-in-python-f77d5718061a
#MachineLearning #DeepLearning #DataScience #ArtificialIntelligence #AI

GitHub Typo Corpus: A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors
Masato Hagiwara, Masato Mita : https://arxiv.org/abs/1911.12893
Code & Dataset https://github.com/mhagiwara/github-typo-corpus
#ArtificialIntelligence #DeepLearning #NLP

Buffalo University Comprehensive Lecture Slides for Machine Learning and Deep Learning

By Professor Sargur Srihari

Machine Learning:
https://cedar.buffalo.edu/~srihari/CSE574/

Deep Learning:
https://cedar.buffalo.edu/~srihari/CSE676/index.html

Probabilistic Graphical Models:
https://cedar.buffalo.edu/~srihari/CSE674/

Data Mining:
https://cedar.buffalo.edu/~srihari/CSE626/index.html

#machinelearning #deeplearning #datamining #AI #artificialintelligence

Mathematicians and neuroscientists have created the first anatomically accurate model that explains how vision is possible.
join

@ArtificialIntelligenceArticles
https://www.quantamagazine.org/a-mathematical-model-unlocks-the-secrets-of-vision-20190821/

SSL FTW!
Pretext-Invariant Representation Learning: a self-supervised method based on Siamese nets for visual feature learning from FAIR.
Beats supervised pre-training & all previous SSL methods on ImageNet, VOC-07-12, etc. https://arxiv.org/abs/1912.01991

Major trends in #NLP : a review of 20 years of #ACL research

The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) is starting this week in Florence, Italy. We took the opportunity to review major research trends in the animated NLP space and formulate some implications from the business perspective. The article is backed by a statistical and — guess what — NLP-based analysis of ACL papers from the last 20 years

https://towardsdatascience.com/major-trends-in-nlp-a-review-of-20-years-of-acl-research-56f5520d473

RGPNet: A Real-Time General Purpose Semantic Segmentation
Elahe Arani, Shabbir Marzban, Andrei Pata, Bahram Zonooz : https://arxiv.org/abs/1912.01394
#ArtificialIntelligence #DeepLearning #MachineLearning

Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes
Greg Yang : https://arxiv.org/abs/1910.12478
#ArtificialIntelligence #DeepLearning #NeurIPS2019

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
Hendrycks et al.: https://arxiv.org/abs/1912.02781
#ArtificialIntelligence #DeepLearning #MachineLearning

https://www.youtube.com/watch?v=mckulxKWyoc
Raia Hadsell - Deep Reinforcement Learning & Real World Challenges - YouTube

Generate realistic and diverse images using this state of the art model released recently (StarGAN v2)!

https://www.profillic.com/paper/arxiv:1912.01865

PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke et al.: https://arxiv.org/abs/1912.01703
#ArtificialIntelligence #deepLearning #PyTorch

Pre-Debate Material
"Deep Learning was great, what's next?"
What is missing to extend Deep Learning to reach human-level AI? Yoshua Bengio : https://youtu.be/cBt5EvHRS5M?t=70
RSVP (2320 people already signed up) at https://bengio-marcus.eventbrite.ca
#AIDebate #ArtificialIntelligence #DeepLearning

PhiFlow
Research-oriented differentiable fluid simulation framework : https://github.com/tum-pbs/PhiFlow
#ArtificialIntelligence #MachineLearning #TensorFlow

Understanding Transfer Learning for Medical Imaging
https://ai.googleblog.com/2019/12/understanding-transfer-learning-for.html

Capsule Routing via Variational Bayes (AAAI 2020)

Hi guys, check out our new paper on Capsule networks to appear in AAAI 2020.

[https://arxiv.org/pdf/1905.11455.pdf](https://arxiv.org/pdf/1905.11455.pdf)

Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs
Alexia Jolicoeur-Martineau and Ioannis Mitliagkas : https://arxiv.org/abs/1910.06922
Blog : https://ajolicoeur.wordpress.com/MaximumMarginGAN
Code : https://github.com/AlexiaJM/MaximumMarginGANs
#SupportVectorMachines #GANs #ArtificialIntelligence

A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models
Kuznetsov et al.: https://arxiv.org/abs/1910.13148
#MachineLearning #NeurIPS #NeurIPS2019