Facebook at NeurIPS 2018
https://research.fb.com/facebook-at-neurips-2018/

After Google released Bilateral NN for image enhancement (https://groups.csail.mit.edu/graphics/hdrnet/) they not stopped and created new cool Night Sight algorithm (https://www.theverge.com/2018/11/14/18092660/google-night-sight-review-pixel-2-3-camera-photos-image-quality)
Hope we will see paper for it soon))

Great second talk in the Algorithmic Fairness session on de-biasing image classification datasets. Reported on the results of the Inclusive Images Kaggle competition.

https://www.kaggle.com/c/inclusive-images-challenge
#NeurIPS2018

Deep Counterfactual Regret Minimization arxiv.org/abs/1811.00164

Top 100 most discussed academic papers (across all fields) for 2017
https://goo.gl/b8Jxip @ArtificialIntelligenceArticles

Novel Bayesian inference framework that combines variational inference with active-sampling Bayesian quadrature for models with expensive black-box likelihood https://arxiv.org/pdf/1810.05558.pdf

SLIDES
Model-Based Deep Reinforcement Learning
https://people.eecs.berkeley.edu/~cbfinn/_files/mbrl_cifar.pdf

Gary Marcus: The Deepest Problem with Deep Learning...

https://medium.com/@GaryMarcus/the-deepest-problem-with-deep-learning-91c5991f5695

AlphaFold: Using AI for scientific discovery

By DeepMind: https://deepmind.com/blog/alphafold/

#machinelearning #artificialintelligence #discovery #science #technology

NeurIPS | 2018

Tutorial on Adversarial Robustness: Theory and Practice

Accompanying notes and code: https://adversarial-ml-tutorial.org/

So you want to be a Research Scientist?
Things they don’t teach you in graduate school
1. Research is about ill-posed questions with multiple (or no) answers

https://medium.com/@vanhoucke/so-you-want-to-be-a-research-scientist-363c075d3d4c

7 TED Talks on Artificial Intelligence to Watch in 2018


https://www.marktechpost.com/2018/12/03/7-ted-talks-on-artificial-intelligence-to-watch-in-2018/

Foot Pressure from Video: A Deep Learning Approach to Predict Dynamics from Kinematics. https://arxiv.org/abs/1811.12607

Macro action selection with deep reinforcement learning in StarCraft

Xu et al.: https://arxiv.org/abs/1812.00336

#artificialintelligence #deeplearning #machinelearning #reinforcementlearning #techno

Slides from #NeurIPS2018 Tutorial on causal inference

By Susan Athey: https://goo.gl/eBRFzv

Reading Minds with Deep Learning
https://blog.floydhub.com/reading-minds-with-deep-learning/

NeurIPS2018 Best Paper award https://papers.nips.cc/paper/8200-non-delusional-q-learning-and-value-iteration @ArtificialIntelligenceArticles

NeurIPS2018 Best Paper award https://arxiv.org/abs/1806.00291 @ArtificialIntelligenceArticles

NeurIPS2018 Best Paper award https://arxiv.org/abs/1806.07366 @ArtificialIntelligenceArticles

NeurIPS2018 Best Paper award https://papers.nips.cc/paper/7601-nearly-tight-sample-complexity-bounds-for-learning-mixtures-of-gaussians-via-sample-compression-schemes