SpecXAI -- Spectral interpretability of Deep Learning Models
Deep learning is becoming increasingly adopted in business and industry due to its ability to transform large quantities of data into high-performing models. These models, however, are generally regarded as black boxes, which, in spite of their performance, could prevent their use. In this context, the field of eXplainable AI attempts to develop techniques that temper the impenetrable nature of the models and promote a level of understanding of their behavior. Here we present our contribution to XAI methods in the form of a framework that we term SpecXAI, which is based on the spectral characterization of the entire network. We show how this framework can be used to not only understand the network but also manipulate it into a linear interpretable symbolic representation.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep LearningExplainable Artificial Intelligence (XAI)Similar Papers 제목 키워드 기반
Balancing Interpretability and Performance in Reinforcement Learning: An Adaptive Spectral Based Linear Approach
Reinforcement learning (RL) has been widely applied to sequential decision making, where interpretability and performance are both critical for practical adoption. Current approaches typically focus on performance and re…
Reinforcement LearningDecision MakingA Unified Matrix-Spectral Framework for Stability and Interpretability in Deep Learning
We develop a unified matrix-spectral framework for analyzing stability and interpretability in deep neural networks. Representing networks as data-dependent products of linear operators reveals spectral quantities govern…
A Deep Equilibrium Network for Hyperspectral Unmixing
Hyperspectral unmixing (HU) is crucial for analyzing hyperspectral imagery, yet achieving accurate unmixing remains challenging. While traditional methods struggle to effectively model complex spectral-spatial features, …
FOCUS: Fused Observation of Channels for Unveiling Spectra
Hyperspectral imaging (HSI) captures hundreds of narrow, contiguous wavelength bands, making it a powerful tool in biology, agriculture, and environmental monitoring. However, interpreting Vision Transformers (ViTs) in t…
Gaussian Processes on Graphs via Spectral Kernel Learning
We propose a graph spectrum-based Gaussian process for prediction of signals defined on nodes of the graph. The model is designed to capture various graph signal structures through a highly adaptive kernel that incorpora…
Gaussian Processes