Deep Learning in the Brain, by Blake Richards. Nice to think about whether backprop-ish processes happen in brains. https://www.youtube.com/watch?v=dZwB5Mj-PPM

"Training Neural Nets on Larger Batches: Practical Tips for 1-GPU, Multi-GPU & Distributed setups"
https://goo.gl/tUysBT

Self-Attention Generative Adversarial Networks"

Tensorflow implementation: https://github.com/brain-research/self-attention-gan

Paper by Zhang et al.: https://arxiv.org/abs/1805.08318

Synthesizing Tabular Data using Generative Adversarial Networks

By Lei Xu, Kalyan Veeramachaneni: https://arxiv.org/abs/1811.11264

Robust Artificial Intelligence and Robust Human Organizations

Thomas G. Dietterich: https://arxiv.org/abs/1811.10840

MIT can now reproduce paintings using deep learning & 3D-printing https://news.mit.edu/2018/mit-csail-repaint-system-reproducing-paintings-make-impression-1129

A Complete Guide to Choosing the Best #MachineLearning Course https://www.kdnuggets.com/2018/11/simplilearn-complete-guide-machine-learning-course.html

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