The first AI universe sim is fast and accurate—and its creators don't know how it works
https://phys.org/news/2019-06-ai-universe-sim-fast-accurateand.html

Applied Data Scientists / Quantitative Researchers (Stats, Machine Learning, Python/R) @ YELP London

We are looking for Applied Data Scientists / Quantitative Researchers to join our London team. Please visit the

following link for more info: https://www.yelp.com/careers/job-openings/8c288764-b1db-4b72-ac75-07b34c9f9b74

For those who are interested in healthcare : Machine Learning for Healthcare 2019
https://www.mlforhc.org/

Training an AI agent to play Snake with TensorFlow 2.0.

Code by Paweł Kieliszczyk: https://github.com/pawel-kieliszczyk/snake-reinforcement-learning

#MachineLearning #ReinforcementLearning #TensorFlow

Fast Training of Sparse Graph Neural Networks on Dense Hardware

Balog et al.: https://arxiv.org/abs/1906.11786

#artificialintelligence #neuralnetworks #machinelearning #datascience

Growing Action Spaces
Farquhar et al.: https://arxiv.org/abs/1906.12266
#deeplearning #machinelearning #reinforcementlearning

PhD fellow in Theoretical Machine Learning
University of Copenhagen, Denmark

More Details: https://www.marktechpost.com/job/phd-fellow-in-theoretical-machine-learning/
Department of Computer Science, Faculty of Science at University of Copenhagen is offering a PhD scholarship in Theoretical Machine Learning commencing 01.10.2019 or as soon as possible thereafter.

How do neural networks see depth in single images?
https://arxiv.org/pdf/1905.07005.pdf

Robots that Learn to Adapt
https://bair.berkeley.edu/blog/2019/05/06/robot-adapt/

"Machine Learning Systems are Stuck in a Rut"
By Paul Barham and Michael Isard: https://dl.acm.org/citation.cfm?id=3321441
#TensorFlow #PyTorch #Swift #MachineLearning

Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks
Maksym Andriushchenko and Matthias Hein: https://arxiv.org/abs/1906.03526
Code: https://github.com/max-andr/provably-robust-boosting
#MachineLearning #Cryptography #Security

Deep Neuroevolution of Recurrent and Discrete World Models

https://arxiv.org/abs/1906.08857

Post-Doctoral Fellowship on Automatic Machine Learning (AutoML)

We are seeking a highly creative and motivated post-doctoral fellow to join the Data Mining Group at the Eindhoven University of Technology. The candidate will be working in collaboration with Dr. ir. Joaquin Vanschoren, as well as the OpenML core team and Amazon Research.

The field of automated machine learning (AutoML) aims to automatically build machine learning models in a data-driven, objective, and automatic way. We are building an AutoML playground (an 'AutoML Gym') to train AutoML systems on many different problems and get increasingly better over time. Similar to the OpenAI Gym, which trains reinforcement learning agents on many different scenario's, the AutoML Gym will train and test many different AutoML systems (agents) on many challenging problems. We will continuously track the performance of the AutoML agents, and store this information in a meta-data repository, a shared memory that can be accessed by any AutoML agent to perform meta-learning and become increasingly better over time.

This work is funded by an Amazon Research Award. It will be set in a very interactive environment, including the Eindhoven Data Mining Group, the OpenML team, the AutoML community, and Amazon research.

To apply, please submit requested documents at https://jobs.tue.nl/en/vacancy/postdoctoral-fellow-on-automatic-machine-learning-661204.html. For further questions, please contact dr. Joaquin Vanschoren by e-mail ([email protected]).

We will start processing applications as of July 15th 2019, and until the position is filled. The fellow can start as soon as possible.

Thanks for disseminating this opportunity,
Joaquin Vanschoren

Overview of deep learning in medical imaging


https://link.springer.com/article/10.1007%2Fs12194-017-0406-5

How do neural networks see depth in single images?

Paper link: arxiv.org/abs/1905.07005

6 thoughts on “The “η-trick” or the effectiveness of reweighted least-squares”
https://francisbach.com/the-%ce%b7-trick-or-the-effectiveness-of-reweighted-least-squares/

Neural Decipherment via Minimum-Cost Flow: from Ugaritic to Linear B
Luo et al.: https://arxiv.org/abs/1906.06718
#ArtificialIntelligence #Computation #Language