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Matryoshka Representation Learning (MRL) is a super exciting approach to improving the quality and efficiency of embedding models and strategies ✨ MRL allows models to store more information in the earlier dimensions of their data vectors. This method not only boosts performance in tasks like classification and retrieval, but... show more

Victoria Slocum

9,845 subscribers

120,470 Aufrufe • vor 1 Jahr •via X (Twitter)

Kunst Wissenschaft & Technologie Bildung

Anya Rossi• Live Now

Private livecam show

9 Kommentare

Profilbild von Parul Pandey

Parul Pandeyvor 1 Jahr

Great video and explanation. Also love how you color-coded the equation to make it simple to understand 💡

Profilbild von Leonie

Leonievor 1 Jahr

Love the new format! 🔥🔥🔥

Profilbild von Philip Vollet

Philip Volletvor 1 Jahr

seesh 9000

Profilbild von Edward

Edwardvor 1 Jahr

This video is

Profilbild von Suman Das 👨🏽‍💻 ⚽

Suman Das 👨🏽‍💻 ⚽vor 1 Jahr

Thanks for sharing 🙌

Profilbild von Radheshyam

Radheshyamvor 1 Jahr

Awesome explanation thanks 🙏

Profilbild von Species ⚡️ 🌘 🌍

Species ⚡️ 🌘 🌍vor 1 Jahr

How do you make video like this? Shareable?

Profilbild von Victoria Slocum

Victoria Slocumvor 1 Jahr

Green screen, Adobe Premiere, and lots of failed attempts and practice 😅

Profilbild von Flavio Milazzo, MSc

Flavio Milazzo, MScvor 1 Jahr

Great share, thank you.

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New PNAS paper. Historical GDP per capita data is scarce, but data on the places of birth, death, and occupations of famous individuals is abundant. In this paper we estimate the historical GDP per capita of hundreds of regions in Europe and North America using a machine learning model that leveraged data on about 500k famous biographies. Our estimates more-or-less quadruple the availability of historical GDP per capita estimates for the last 700 years. So why use biographies to augment historical GDP per capita data? Biographical data contains information about people who might have contributed directly to economic growth, like James Watt, or that were attracted to wealthy places looking for patrons, like Michelangelo. So we--mainly Philipp (Philipp Koch)--used this data to construct hundreds of features describing each European region. Then, we trained a machine learning model to find the features that explained most of the variance in a cross-validation test, where we split regions multiple times into a training set and a test set. On average, the model explained about 90% of the variance in GDP per capita of the regions it had not seen during training. But we wanted to go further, and Philipp really went to town by looking at different ways to validate our estimates. We found our estimates correlate positively with historical measures of wellbeing, church building activity, urbanization, and body height. We also used these measures to reproduce the basic Atlantic trade result of Acemoglu, Johnson, and Robison and to explore the economic consequences of the famous Lisbon earthquake of 1755. But what I personally loved most about this project, other than working with Philipp Koch and V, is that it shows that we can use machine learning methods not only to explore the future, but the past. There is a bright and growing future in the use of machine learning for economic history. Hope you enjoy the paper and the data. You can find links to the paper and a data exploration tool in the first comment.

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227,586 Aufrufe • vor 3 Jahren