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Transformers for Tabular Data at Capital One with Bayan Bruss - #591

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Content provided by TWIML and Sam Charrington. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by TWIML and Sam Charrington or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ro.player.fm/legal.

Today we’re joined by Bayan Bruss, a Sr. director of applied ML research at Capital One. In our conversation with Bayan, we dig into his work in applying various deep learning techniques to tabular data, including taking advancements made in other areas like graph CNNs and other traditional graph mining algorithms and applying them to financial services applications. We discuss why despite a “flood” of innovation in the field, work on tabular data doesn’t elicit as much fanfare despite its broad use across businesses, Bayan’s experience with the difficulty of making deep learning work on tabular data, and what opportunities have been presented for the field with the emergence of multi-modality and transformer models. We also explore a pair of papers from Bayan’s team, focused on both transformers and transfer learning for tabular data.

The complete show notes for this episode can be found at twimlai.com/go/591

  continue reading

700 episoade

Artwork
iconDistribuie
 
Manage episode 340930730 series 2355587
Content provided by TWIML and Sam Charrington. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by TWIML and Sam Charrington or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ro.player.fm/legal.

Today we’re joined by Bayan Bruss, a Sr. director of applied ML research at Capital One. In our conversation with Bayan, we dig into his work in applying various deep learning techniques to tabular data, including taking advancements made in other areas like graph CNNs and other traditional graph mining algorithms and applying them to financial services applications. We discuss why despite a “flood” of innovation in the field, work on tabular data doesn’t elicit as much fanfare despite its broad use across businesses, Bayan’s experience with the difficulty of making deep learning work on tabular data, and what opportunities have been presented for the field with the emergence of multi-modality and transformer models. We also explore a pair of papers from Bayan’s team, focused on both transformers and transfer learning for tabular data.

The complete show notes for this episode can be found at twimlai.com/go/591

  continue reading

700 episoade

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