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

Today we’re joined by Bayan Bruss, Vice President of Applied ML Research at Capital One. In our conversation with Bayan, we covered a pair of papers his team presented at this year’s ICML conference. We begin with the paper Interpretable Subspaces in Image Representations, where Bayan gives us a dive deep into the interpretability framework, embedding dimensions, contrastive approaches, and how their model can accelerate image representation in deep learning. We also explore GOAT: A Global Transformer on Large-scale Graphs, a scalable global graph transformer. We talk through the computation challenges, homophilic and heterophilic principles, model sparsity, and how their research proposes methodologies to get around the computational barrier when scaling to large-scale graph models.The complete show notes for this episode can be found at twimlai.com/go/641.

Bayan Bruss, the VP of AI Foundations at Capital One, collaborates with academic researchers to apply cutting-edge AI in finance. He delves into the complexities of out-of-distribution (OOD) detection, emphasizing its significance for safety in AI applications. The discussion also covers the challenges of integrating AI research into corporate environments, the need for model explainability, and the cautious adoption of generative AI. Bruss highlights innovations and limitations in current AI models, underlining the importance of real-world testing to ensure robustness and reliability.