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

Human Pose Estimation 101

By Sudharshan Chandra Babu: https://github.com/cbsudux/Human-Pose-Estimation-101

CosmoFlow: Using Deep Learning to Learn the Universe at Scale
arxiv.org/abs/1808.04728 #deeplearning #physics #universe

Imperial College Mathematics department Deep Learning course

From scratch to BigGANs : https://www.deeplearningmathematics.com

#artificialintelligence #deeplearning #mathematics

Google DeepMind founder Demis Hassabis: Three truths about AI

1) AI will save us from ourselves
2) AI will lead to Nobel-prize winning science breakthroughs
3) Deep learning is not enough to crack AI


video : https://www.techrepublic.com/videos/artificial-intelligence-is-vital-for-business-but-its-no-magic-bullet/


https://www.techrepublic.com/google-amp/article/google-deepmind-founder-demis-hassabis-three-truths-about-ai/

https://t.iss.one/ArtificialIntelligenceArticles

Announcing fast.ai part 1 now available as Kaggle Kernels

Blog by William Horton : https://medium.com/@hortonhearsafoo/announcing-fast-ai-part-1-now-available-as-kaggle-kernels-8ef4ca3b9ce6

Where Did My Optimum Go? : An Empirical Analysis of Gradient Descent Optimization in Policy Gradient Methods

Henderson et al.: https://arxiv.org/abs/1810.02525

ArviZ: Exploratory analysis of Bayesian models

Includes functions for posterior analysis, model checking, comparison and diagnostics.

GitHub : https://github.com/arviz-devs/arviz