Gaze360: Physically Unconstrained Gaze Estimation in the Wild
Kellnhofer et al.: https://arxiv.org/abs/1910.10088
Website : https://gaze360.csail.mit.edu
GitHub : https://github.com/Erkil1452/gaze360
#ArtificialIntelligence #DeepLearning #MachineLearning

Using machine learning and information visualisation for discovering latent topics in Twitter news
Vargas-Calderon et al.: https://arxiv.org/abs/1910.09114
#ArtificialIntelligence #MachineLearning #SocialNetworks

Quantum Supremacy Using a Programmable Superconducting Processor
Blog by John Martinis and Sergio Boixo : https://ai.googleblog.com/2019/10/quantum-supremacy-using-programmable.html
#QuantumComputer #QuantumPhysics #QuantumSupremacy

pdf: https://openaccess.thecvf.com/content_CVPR_2019/papers/Gu_Mask-Guided_Portrait_Editing_With_Conditional_GANs_CVPR_2019_paper.pdf

github repo: https://github.com/cientgu/Mask_Guided_Portrait_Editing

Generating Sequences With Recurrent Neural Networks
Paper: https://arxiv.org/pdf/1308.0850.pdf
This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time. The approach is demonstrated for text (where the data are discrete) and online handwriting (where the data are real-valued). It is then extended to handwriting synthesis by allowing the network to condition its predictions on a text sequence. The resulting system is able to generate highly realistic cursive handwriting in a wide variety of styles.

Deep learning might not be an end solution for all the problems, but it isn't going away soon.
"deep learning must be augmented with some operations borrowed from classical symbolic systems, which is to say we need hybrid models, which take the best of classical AI (which allows for explicit representation of hierachical structure and abstract rules) and combine that with the strengths of deep learning." - Gary Marcus
Blog: https://medium.com/@GaryMarcus/bengio-v-marcus-and-the-past-present-and-future-of-neural-network-models-of-language-b4f795ff352b
Further reading: https://arxiv.org/pdf/1810.08272.pdf

Deep learning algorithm helps diagnose neurological emergencies
https://physicsworld.com/a/deep-learning-algorithm-helps-diagnose-neurological-emergencies/

Anatomy of Matplotlib
Tutorial developed for the SciPy conference : https://github.com/matplotlib/AnatomyOfMatplotlib
#Matplotlib #DataScience #DeepLearning

Learning Partial Differential Equations from Data Using Neural Networks
Hasan et al.: https://arxiv.org/abs/1910.10262
#DifferentialEquations #MachineLearning #NeuralNetworks

An article in le figaro on the occasion of the release of Yann LeCun book.

https://www.lefigaro.fr/vox/economie/charles-jaigu-ce-que-l-ia-dit-de-notre-cerveau-20191023

AI Benchmark: All About Deep Learning on Smartphones in 2019
https://arxiv.org/abs/1910.06663

Google's Quantum advancements...
"A programmable quantum computer has been reported to outperform the most powerful conventional computers in a specific task — a milestone in computing comparable in importance to the Wright brothers’ first flights" - William D. Oliver

https://www.nature.com/articles/d41586-019-03173-4

ICYMI: Faceswap+ "Mask-Guided Portrait Editing with Conditional GANs"

https://www.profillic.com/paper/arxiv:1905.10346

They address three issues in existing techniques: diversity, quality, and controllability for portrait synthesis and editing.

The Consciousness Prior
Yoshua Bengio : https://arxiv.org/abs/1709.08568
#MachineLearning #DeepLearning #ArtificialIntelligence

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
Yu et al.: https://arxiv.org/abs/1910.10897
#MachineLearning #ArtificialIntelligence #Robotics

Learning to Smell: Using Deep Learning to Predict the Olfactory Properties of Molecules
By Alexander B Wiltschko : https://ai.googleblog.com/2019/10/learning-to-smell-using-deep-learning.html
#DeepLearning #Olfactory #Molecules

Check out our new paper with Fei Deng and Zhuo Zhi on "Generative Hierarchical Modeling of Parts, Objects, and Scenes" https://arxiv.org/pdf/1910.09119.pdf

We learn compositional and interpretable probabilistic scene graphs from images in an unsupervised way via generation