Open Questions about Generative Adversarial Networks https://distill.pub/2019/gan-open-problems/ https://t.iss.one/ArtificialIntelligenceArticles

Repurposing CNNs - from images to sound:

Cool article from 2017 by Hershey et al. (https://arxiv.org/abs/1609.09430). Those nice folks took best of the best CNN for image recognition and repurposed them to identify audios. And, no wander, they succeeded.

What else looks like audio? Right, seismograms! Now I can't wait to implement ResNet for earthquake classification (which is already done btw

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