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Entity Embeddings

Entity Embeddings is a technique for applying deep learning to tabular data. It involves representing the categorical data of an information systems entity with multiple dimensions. It is being used in several production systems at companies such as OpenAI, Google, Instacart, Twitter, etc.

Kaytek Founder Director Mr Mahesh Khatri has spoken on Entity Embeddings at various global forums.

Youtube Presentation on Entity Embeddings at a Deep Learning Meetup held at Amazon's office in New York, USA.

More Event Photographs (courtesy Kris Skrinak)

Kaytek Founder Director Mr Mahesh Khatri spoke at the Deep Learning NYC Meetup Group in New York, USA on Thursday 6th June 2019 on the topic Entity Embeddings & Pytorch in the Enterprise". The event was organized by Mr Kris Skrinak of Amazon Web Services (AWS) & Ms Pallavi Gadgil,the leaders of the Deep Learning NYC Meetup Group. Thanks to them and also Amazon for hosting the above meet at their New York office. Also, a big thanks to to all the participants who came and actively interacted on the topic.

Presentation Overview - In his presentation, Mr Mahesh Khatri presented the concept along with examining it's usage in the following 3 papers :

Kaggle Competition winner papers - Artificial Neural Networks Applied to Taxi Destination Prediction (Yoshua Bengio’s team - 31st July 2015) &

Entity Embeddings of Categorical Variables (22nd April 2016) &

Google Research paper - Deep Neural Networks for YouTube Recommendations (16th September 2016).

Youtube Video Contents & Timeline

0:00 - Why Talk of Entity Embeddings ?

2:45 - What Are Entity Embeddings ?

8:32 - Importance of Entity Embeddings

9:34 - 2 Perspectives - Word Embeddings & Real World Tabular Data

10:15 - Word Embeddings

27:00 - Real World Tabular Data

34:55 - Machine Learning Library Support

39:39 - Artificial Neural Networks Applied to Taxi Destination Prediction

42:33 - Entity Embeddings of Categorical Variables

45:24 - Deep Neural Networks for YouTube Recommendations

52:30 - Industry Usage - Twitter, OpenAI, Healthcare, etc

55:13 - Articles, Summary, Call To Action

FastAI References in the above talk :

14:07 — Size of Embedding

26:23 & 35:35 — Fastai Library Support function — add_datepart

37:38 — Jeremy Howard on Embedding size

44:37 — Rachel Thomas on ‘Rossman Stores Competition’ paper

52:30 — Jeremy Howard on commercial & scientific opportunities

Medium Article on the above talk - An Entity Embeddings sharing with New York's AI Community - 29th June 2019.

Application of Entity Embeddings - Article Collaborative Filtering — Understanding embeddings in User Movie Ratings - 20th December 2018.

Entity Embeddings Deep Dive - Entity Embeddings are used by some of the largest and smartest organizations on the planet like Amazon, Facebook, Google, Twitter and many more in their gigantic internal production scale machine learning systems. TwimlAI (This Week in Machine Learning & AI) just published Mahesh Khatri's presentation on Entity Embedding Deep Dive at their North America Meetup on 13th November 2018.

An explanation of Entity Embeddings & usage in the following research papers was covered :

- Cheng Guo's paper - Entity Embeddings of Categorical Variables which was a 3rd prize winner at a Kaggle competion

- Yoshua Bengio's paper - Artificial Neural Networks Applied to Taxi Destination Prediction which won the 1st prize at the ECML/PKDD discovery challenge &

- Google AI Research paper - Deep Neural Networks for YouTube Recommendations

All of those who are interested in learning about contemporary usage of Entity Embeddings may find the presentation useful. A Basic Knowledge of Neural Networks is needed for understanding this presentation. Thanks to Sam Charrington & TwimlAI for their support in hosting this event. - 10th December 2018.

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Last updated on 12th June 2023.

Created on 16th January 2020.