Rules of Machine Learning: Best Practices for ML Engineering

By Martin Zinkevich: https://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf

TherML: The Thermodynamics of Machine Learning

Anonymous: https://openreview.net/forum?id=HJeQToAqKQ&noteId=HJeQToAqKQ @ArtificialIntelligenceArticles

Understanding & Generalizing AlphaGo Zero #ICLR2019

Anonymous: https://openreview.net/forum?id=rkxtl3C5YX

Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks [106pp]

Reviews recent literature in a comprehensive way, and provides an in-depth view of recent advances, current challenges, problem extensions and datasets.

https://arxiv.org/abs/1809.03193 @ArtificialIntelligenceArticles

RTX 2080 Ti Deep Learning Benchmarks
https://lambdalabs.com/blog/2080-ti-deep-learning-benchmarks/

How to train your MAML

By Anonymous: https://openreview.net/forum?id=HJGven05Y7

'TL;DR: MAML is great, but it has many problems, we solve many of those problems and as a result we learn most hyper parameters end to end, speed-up training and inference and set a new SOTA in few-shot learning'

#metalearning #deeplearning #fewshotlearning

"Large scale GAN training for high fidelity natural image synthesis":

Paper: https://openreview.net/pdf?id=B1xsqj09Fm

Samples:https://drive.google.com/drive/folders/1lWC6XEPD0LT5KUnPXeve_kWeY-FxH002

On the Turing Completeness of Modern Neural Network Architectures

By Anonymous: https://openreview.net/forum?id=HyGBdo0qFm @ArtificialIntelligenceArticles

An Introduction to Probabilistic Programming

By Jan-Willem van de Meent, Brooks Paige, Hongseok Yang, Frank Wood: https://arxiv.org/abs/1809.10756 @ArtificialIntelligenceArticles

Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions https://openreview.net/forum?id=r1eEG20qKQ

Manipulating Attributes of Natural Scenes via Hallucination

https://web.cs.hacettepe.edu.tr/~karacan/projects/attribute_hallucination/ @ArtificialIntelligenceArticles

A Review of the Recent History of Natural Language Processing

By Sebastian Ruder: https://blog.aylien.com/a-review-of-the-recent-history-of-natural-language-processing/

Why Momentum Really Works

By Gabriel Goh: https://distill.pub/2017/momentum/

#artificialintelligence #deeplearning #machinelearning

Search ICLR 2019

Having trouble finding the papers that use technique X, dataset D, or cite author ME in the #ICLR2019 submissions?

Search ICLR 2019: https://search.iclr2019.smerity.com/

Free online textbook of Jupyter notebooks for fast.ai

Computational Linear Algebra course
https://github.com/fastai/numerical-linear-algebra

Generative Ensembles for Robust Anomaly Detection

By Hyunsun Choi, Eric Jang: https://arxiv.org/abs/1810.01392

New TPU example

Fashion MNIST in Colab: https://colab.research.google.com/github/tensorflow/tpu/blob/master/tools/colab/fashion_mnist.ipynb

Probabilistic Meta-Representations Of Neural Networks

Karaletsos et al. : https://www.gatsby.ucl.ac.uk/~balaji/udl-camera-ready/UDL-13.pdf

SOTAWHAT - A script to keep track of state-of-the-art AI research

Post: https://huyenchip.com/2018/10/04/sotawhat.html
GitHub: https://github.com/chiphuyen/sotawhat

Why we need to continue to try to better understand human/animal brains for #AI research. "Neuroscience-Inspired Artificial Intelligence" https://www.cell.com/neuron/fulltext/S0896-6273(17)30509-3

Connections between physics and deep learning
https://www.youtube.com/watch?v=5MdSE-N0bxs @ArtificialIntelligenceArticles