Jibril Frej

MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks

2023 · arXiv (Cornell University)

Predicting multiple real-world tasks in a single model often requires a particularly diverse feature space. Multimodal (MM) models aim to extract the synergistic predictive potential of multiple data types to create a shared feature space …

Intrinsic User-Centric Interpretability through Global Mixture of Experts

2024 · arXiv (Cornell University)

In human-centric settings like education or healthcare, model accuracy and model explainability are key factors for user adoption. Towards these two goals, intrinsically interpretable deep learning models have gained popularity, focusing on accurate predictions alongside …