Explainable and interpretable multimodal large language models: A comprehensive survey

Y Dang, K Huang, J Huo, Y Yan, S Huang, D Liu… - ar** Its Surpasser: A Survey
M Liu, D Yang, FR Beyette, S Wang… - 2024 IEEE Signal …, 2024 - ieeexplore.ieee.org
Transformers have significantly impacted machine learning, particularly in natural language
processing and computer vision, due to their robust attention mechanisms and scalability …

ViTmiX: Vision Transformer Explainability Augmented by Mixed Visualization Methods

E Hogea, DM Onchis, A Coporan, AM Florea… - arxiv preprint arxiv …, 2024 - arxiv.org
Recent advancements in Vision Transformers (ViT) have demonstrated exceptional results
in various visual recognition tasks, owing to their ability to capture long-range dependencies …

Visual Affordance Recognition: A Study on Explainability and Interpretability for Human Robot Interaction

R Bhattacharyya, A Bhowmick, SM Hazarika - Discovering the Frontiers of …, 2024 - Springer
Deep learning-based affordance research is motivated by the need to achieve higher
degrees of automation and accuracy. On the contrary, the concept of affordance originated …

A Unified Framework for Interpretable Transformers Using PDEs and Information Theory

Y Zhang - arxiv preprint arxiv:2408.09523, 2024 - arxiv.org
This paper presents a novel unified theoretical framework for understanding Transformer
architectures by integrating Partial Differential Equations (PDEs), Neural Information Flow …