Step Change Improvement in Molecular Property Prediction with PotentialNet

Paper on a significant improvement in ability to predict molecular properties in drug design. #ML algorithms are getting better and better than classical methods.

Link: https://medium.com/@pandelab/step-change-improvement-in-molecular-property-prediction-with-potentialnet-f431ffa32a2c

#drugsdesign #biolearning #healthcare

Remember the black hole in the movie Interstellar? Turns out it was accurately modelled using Einstein's equations and 40000 lines of C++ code... and there's a full-on physics paper describing their process here: https://arxiv.org/pdf/1502.03808.pdf


#astrophysics #GravitationalLensing

Unsupervised learning: the curious pupil"
Unsupervised learning, a paradigm for creating artificial intelligence that learns about data without a particular task in mind: learning for the sake of learning.
Blog by Alexander Graves and Kelly Clancy, DeepMind: https://deepmind.com/blog/unsupervised-learning/
#artificialintelligence #deeplearning #unsupervisedlearning

Katie Bouman TED Talk
Katie Bouman is the postdoctoral fellow who led the development of the algorithm used to image a black hole.
TED Talk:

https://www.ted.com/talks/katie_bouman_what_does_a_black_hole_look_like

Fast Interactive Object Annotation with Curve-GCN"
Ling et al.
Paper: https://arxiv.org/pdf/1903.06874.pdf
Video: https://www.youtube.com/watch?v=ycD2BtO-QzU
#PyTorch Code: https://github.com/fidler-lab/curve-gcn
#ArtificialIntelligence #DeepLearning #MachineLearning

Best of arXiv.org for AI, Machine Learning, and Deep Learning – March 2019 #insidebigdata #BigDataAnalytics https://insidebigdata.com/2019/04/09/best-of-arxiv-org-for-ai-machine-learning-and-deep-learning-march-2019/

This one is a must read - the latest #ComputerVision #PatternRecognition https://deepai.org/publication/relational-action-forecasting

An overview of embedding models of entities and relationships for knowledge base completion

https://arxiv.org/abs/1703.08098

Liquid Splash Modeling with Neural Networks


https://ge.in.tum.de/download/2018-mlflip-um/2018-mlflip-um-talk.pdf

The Pros and Cons: Rank-aware Temporal Attention for Skill Determination in Long Videos

https://dimadamen.github.io/TheProsandCons/index.html

https://arxiv.org/pdf/1812.05538.pdf

Deep brain stimulation of the internal capsule enhances human cognitive control and prefrontal cortex function
https://www.nature.com/articles/s41467-019-09557-4

Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering. https://arxiv.org/abs/1904.04357

Relational Reasoning Network (RRN) for Anatomical Landmarking. https://arxiv.org/abs/1904.04354

Neural Rerendering in the Wild. https://arxiv.org/abs/1904.04290

Masquer Hunter: Adversarial Occlusion-aware Face Detection
https://arxiv.org/abs/1709.05188v2

best paper awards at Conference NAACL 2019

What's in a Name? Reducing Bias in Bios without Access to Protected Attributes

https://arxiv.org/abs/1904.05233

best paper awards at Conference NAACL 2019

https://naacl2019.org/blog/best-papers/

Analysing Mathematical Reasoning Abilities of Neural Models
Saxton et al.: https://arxiv.org/abs/1904.01557
Code and data: https://github.com/deepmind/mathematics_dataset
#ArtificialIntelligence #MachineLearning #NeuralNetworks

Robots that Learn to Use Improvised Tools
Blog by Annie Xie: https://bair.berkeley.edu/blog/2019/04/11/tools/
#ArtificialIntelligence #DeepLearning #ReinforcementLearning #Robotics

Workshop on Fairness Accountability Transparency and Ethics in Computer Vision at CVPR 2019


https://sites.google.com/view/fatecv/home

Complete ML Study Path On Github
It contains links and resources to learn Tensorflow and Scikit-Learn
https://github.com/clone95/Virgilio @ArtificialIntelligenceArticles