10 Machine Learning Methods that Every Data Scientist Should Know
https://towardsdatascience.com/10-machine-learning-methods-that-every-data-scientist-should-know-3cc96e0eeee9

There is updated version of CS224n which uses pyTorch instead of tf and other updated resources. CS224N: Natural Language Processing with Deep Learning | Winter 2019:
https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z

GPT-2: 1.5B Release
OpenAI : https://openai.com/blog/gpt-2-1-5b-release/
#ArtificialIntelligence #DeepLearning #MachineLearning

The human brain can rewire itself after a traumatic bodily injury, researchers report. Similar findings have been previously reported in animal studies, but this is one of the first studies where such a result has been documented in people.

https://news.missouri.edu/2019/talk-to-the-hand/

Do Deep Neural Networks ‘See’ Faces Like Brains Do?

https://medium.com/syncedreview/do-deep-neural-networks-see-faces-like-brains-do-dc3eef334d79

A deeper dive into UMAP theory
Andy Coenen, Adam Pearce : https://pair-code.github.io/understanding-umap/supplement.html
#ArtificialIntelligence #DeepLearning #MachineLearning

When Artificial Intelligence gets FUNNY with an ability to detect humour & predict LAUGHTER using multimodal language dataset, named UR-FUNNY.
It demonstrated the role of context & punchline in humour detection using TED Talk transcripts with laughter cues for humour analysis.

#EMNLP2019
Read: https://arxiv.org/pdf/1904.06618.pdf
GitHub: https://github.com/ROC-HCI/UR-FUNNY

Learning to Predict Without Looking Ahead: World Models Without Forward Prediction

Rather than hardcoding forward prediction, we try to get agents to *learn* that they need to predict the future.

Check out our #NeurIPS2019 paper!

https://learningtopredict.github.io
https://arxiv.org/abs/1910.13038

NeurIPS 2019: Adversarial music that prevents Amazon Alexa from waking up

https://www.profillic.com/paper/arxiv:1911.00126
They target the attack on the wake-word detection system, jamming the model with some inconspicuous background music to deactivate the VAs while the audio adversary is present

TOP AI & MACHINE LEARNING RESEARCH PAPERS FROM 2019
https://www.topbots.com/top-ml-research-papers-2019/

Query2vec: Search query expansion with query embeddings
https://bytes.grubhub.com/search-query-embeddings-using-query2vec-f5931df27d79

The Measure of Intelligence
François Chollet : https://arxiv.org/abs/1911.01547
GitHub : https://github.com/fchollet/ARC
#ArtificialIntelligence #DeepLearning #MachineLearning

Dynamics-Aware Unsupervised Discovery of Skills
Sharma et al.: https://arxiv.org/abs/1907.01657
#MachineLearning #Robotics #ReinforcementLearning

Deep learning reveals cancer metastasis and therapeutic antibody targeting in whole body
https://www.biorxiv.org/content/biorxiv/early/2019/02/05/541862.full.pdf

A GPT-2 style model for dialog
A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
code https://github.com/microsoft/DialoGPT
paper https://arxiv.org/pdf/1911.00536.pdf

AI meets physics - using artificial neural networks to approximate solutions of the three-body problem.


I'm increasingly intrigued by this paper (https://arxiv.org/pdf/1910.07291.pdf) showing the application of Artificial Neural networks to the infamously insoluble three-body problem in physics, where we try to work out the future position of three objects sometime in the future given Newton's equations of motion. I think it has important implications to how we think about approximation and how we achieve it in practice.

From the authors: "Our results provide evidence that, for computationally challenging regions of phase-space, a trained ANN can replace existing numerical solvers, enabling fast and scalable simulations of many-body systems to shed light on outstanding phenomena such as the formation of black-hole binary systems or the origin of the core collapse in dense star clusters."

https://t.iss.one/ArtificialIntelligenceArticles