Feature attribution methods aim to improve the transparency of deep neural networks by identifying the input features that influence a model's decision. Pixel-based heatmaps have become the standard for attributing features to high-dimensional inputs, such as images, audio representations, and volumes. While intuitive and convenient, these pixel-based attributions fail to capture the underlying structure of the data. Moreover, the choice of domain for computing attributions has often been overlooked. This work demonstrates that the wavelet domain allows for informative and meaningful attributions. It handles any input dimension and offers a unified approach to feature attribution. Our method, the Wavelet Attribution Method (WAM), leverages the spatial and scale-localized properties of wavelet coefficients to provide explanations that capture both the where and what of a model's decision-making process. We show that WAM quantitatively matches or outperforms existing gradient-based methods across multiple modalities, including audio, images, and volumes. Additionally, we discuss how WAM bridges attribution with broader aspects of model robustness and transparency. Project page: https://gabrielkasmi.github.io/wam/
From topology learning to graph generation: A unifying perspective
University of Oxford（牛津大学）；The Chinese University of Hong Kong（香港中文大学）；Shanghai Jiao Tong University（上海交通大学）；University College London（伦敦大学学院）；EPFL（洛桑联邦理工学院）
A Unifying Relational Perspective on Expressive Lottery Tickets
Faculty of Computer Science, University of Vienna（维也纳大学计算机科学学院）；Doctoral School Computer Science, University of Vienna（维也纳大学计算机科学博士学院）；CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences（奥地利科学院CeMM分子医学研究中心）；CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦大学玛丽女王学院电子工程与计算机科学学院）；Research Network Data Science, University of Vienna（维也纳大学数据科学研究网络）
Ideal Attribution and Faithful Watermarks for Language Models
Data Science Institute, University of Chicago（芝加哥大学数据科学研究院）；Paul G. Allen School of Computer Science & Engineering, University of Washington（华盛顿大学保罗·G·阿伦计算机科学与工程学院）
Deep Neural Variation Spaces: A Unifying Perspective on Depth and Complexity
Department of Computer Science University of Wisconsin-Madison（计算机科学系威斯康星大学麦迪逊分校）；Department of Electrical & Computer Engineering University of Wisconsin-Madison（电气与计算机工程系威斯康星大学麦迪逊分校）
