New model to naturally detect depression in conversations

Link: https://news.mit.edu/2018/neural-network-model-detect-depression-conversations-0830

#nlp #audio #dl

Tickets for one of the key conferences #NIPS2018 were sold out in 15 minutes.

NIPS Conference Registrations 2002 thru 2019.
[2018] War erupts for tickets
[2019] AI researchers discover time travel.

#NIPS2018

1st place solution in the recent Home Credit Default Risk #Kaggle competition

- extensive feature engineering, with ~700 of features total used
- XGBoost, LightGBM, CatBoost, FastRGF, DAE+NN, Lin Reg
- 3-level ensembling (stacking x2 + blending)

Link: https://www.kaggle.com/c/home-credit-default-risk/discussion/64821

More on that model in the Wired article: https://thenextweb.com/artificial-intelligence/2018/09/05/mits-depression-detecting-ai-might-be-its-scariest-creation-yet/

The article notes that the best advantage of suggesting approach is that it is context-free.

Those who want to understand #GAN s better can now play with them in the browser with GAN lab.

https://towardsdatascience.com/gan-lab-train-gans-in-the-browser-21a423585460

#Google introduced Conceptual Captions, a new dataset and challenge for image captioning consisting of ~3.3 million image/caption pairs for the machine learning community to train and evaluate their own image captioning models.

Link: https://ai.googleblog.com/2018/09/conceptual-captions-new-dataset-and.html

#dataset

Upcoming spolitght at #NIPS2018 «Realistic Evaluation of Deep Semi-Supervised Learning Algorithms»

Link: https://arxiv.org/abs/1804.09170

OpenAI’s Long Pursuit of Dota 2 Mastery

Full story of the project on medium

https://medium.com/syncedreview/openais-long-pursuit-of-dota-2-mastery-1d3a861472bd

#OpenAI

#netflixresearch has published a YouTube playlist to make people learn about how they improve various aspects of Netflix including personalized algorithms, content valuation, and streaming optimization:

https://www.youtube.com/playlist?list=PL_S_WRR3d9p2J5IILdSS2OGCF8s3E0kjQ

And #Google also launched #DataSet search. This is a huge breakthrough for the DS community, because now it will be easier to access some interesting data.

https://toolbox.google.com/datasetsearch

Predicting Popularity of The New York Times Comments (Part 1)


Article with the #NLP research of NYT comments. Nice example of the applied #research job.

Link: https://towardsdatascience.com/predicting-popularity-of-the-new-york-times-comments-part-1-d32f26261f6f
Github: https://github.com/sakshi716/nyt-nlp-capstone

How to Create Animated Graphs in Python

Short tutorial on how to create animated graphs, which also works in #jupyter notebooks.

Link: https://towardsdatascience.com/how-to-create-animated-graphs-in-python-bb619cc2dec1
Youtube link with #visualization: https://youtu.be/7xrvuSDLHiY|

#novice #beginner

Predicting Popularity of The New York Times Comments (Part 1)


Article with the #NLP research of NYT comments. Nice example of the applied #research job.

Link: https://towardsdatascience.com/predicting-popularity-of-the-new-york-times-comments-part-1-d32f26261f6f
Github: https://github.com/sakshi716/nyt-nlp-capstone

Healthcare Dataset with #PySpark

Back to some #BigData stuff: article about quick-and-dirty development of prediction algorithm using #Spark engine.

Link: https://towardsdatascience.com/healthcare-dataset-with-spark-6bf48019892b

Multimodal Unsupervised Image-to-Image Translation (MUNIT)

Arxiv: https://arxiv.org/abs/1804.04732
Github: https://github.com/NVlabs/MUNIT

Note how posture and scene proportions are retained after transition.

There is a chart for CS research citation rate: https://www.iro.umontreal.ca/~bengioy/citation-rate-CS-1sept2018.html

Looks like the #dl is eating the world of software atm.

#academy #chart

Useful tool for #crowdsourcing annotation and labeling: https://prodi.gy

Something on #SoftwareDevelopment

Data Types From A Machine Learning Perspective With Examples

This is a really #beginner level article about how different data types can be categorized.

Link: https://towardsdatascience.com/scala-for-data-science-engineering-part-1-6a0a5738d7b0