4 papers
MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning
Shay Deutsch, Lionel Yelibi, Alex Tong Lin +1
Deriving meaningful representations from complex, high-dimensional data in unsupervised settings is crucial across diverse machine learning applications. This paper introduces a fr…
Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews
Izunna Okpala, Ashkan Golgoon, Arjun Ravi Kannan
The advent of large language models has ushered in a new era of agentic systems, where artificial intelligence programs exhibit remarkable autonomous decision-making capabilities a…
MBExplainer: Multilevel bandit-based explanations for downstream models with augmented graph embeddings
Ashkan Golgoon, Ryan Franks, Khashayar Filom +1
In many industrial applications, it is common that the graph embeddings generated from training GNNs are used in an ensemble model where the embeddings are combined with other tabu…
Mechanistic interpretability of large language models with applications to the financial services industry
Ashkan Golgoon, Khashayar Filom, Arjun Ravi Kannan
Large Language Models such as GPTs (Generative Pre-trained Transformers) exhibit remarkable capabilities across a broad spectrum of applications. Nevertheless, due to their intrins…