Interpreting the learning dynamics of neural networks can provide useful insights into how networks learn and the development of better training and design approaches. We present an approach to interpret learning in neural networks by measuring relative weight change on a per layer basis and dynamically aggregating emerging trends through combination of dimensionality reduction and clustering which allows us to scale to very deep networks. We use this approach to investigate learning in the context of vision tasks across a variety of state-of-the-art networks and provide insights into the learning behavior of these networks, including how task complexity affects layer-wise learning in deeper layers of networks.
Interpreting weight maps in terms of cognitive or clinical neuroscience: nonsense?
University College London（伦敦大学学院）；Max Planck University College London Centre for Computational Psychiatry and Ageing Research（马克斯·普朗克伦敦大学学院计算精神病学与衰老研究中心）
Interpreting Learning Under Competing Models: Joint and Stepwise Approaches for Dynamic Cognitive Diagnosis
School of Mathematical Sciences, Lancaster University（兰卡斯特大学数学科学学院）；Department of Computer Science, University of Oxford（牛津大学计算机科学系）；Department of Psychology, Lancaster University（兰卡斯特大学心理学系）
Socioeconomic correlations of urban patterns inferred from aerial images: interpreting activation maps of Convolutional Neural Networks
Univ Lyon（里昂大学）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；ENS de Lyon（里昂高等师范学院）；Université Claude Bernard Lyon 1（里昂第一大学克洛德·贝尔纳大学）；Central European University（中欧大学）
Interpretability of Graph Neural Networks to Assess Effects of Global Change Drivers on Ecological Networks
AgroParisTech（巴黎高等农艺科学学院）；INRAE（法国国家农业、食品与环境研究院）；MNHN（法国国家自然历史博物馆）；CNRS（法国国家科学研究中心）；IRD（法国发展研究院）；Institute of Ecology and Environmental Sciences (iEES-Paris)（巴黎生态与环境科学研究所）
HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks
School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；Alibaba-NTU Singapore Joint Research Institute（阿里巴巴-南洋理工大学新加坡联合研究院）
Investigating Learning in Deep Neural Networks using Layer-Wise Weight Change
University of Nebraska（内布拉斯加大学）；University of Nebraska-Lincoln（内布拉斯加大学林肯分校）；Georgia Institute of Technology（佐治亚理工学院）；Ford R&A（福特研究与创新中心）；German University in Cairo（开罗德国大学）
Interpreting Deep Neural Networks for Single-Lead ECG Arrhythmia Classification
Healthcare Technology and Innovation Center (HTIC), Indian Institute of Technology (IIT-M)（印度理工学院马德拉斯分校医疗技术与创新中心）；Indian Institute of Technology, Madras (IITM)（印度理工学院马德拉斯分校）
From Packets to Patterns: Interpreting Encrypted Network Traffic as Longitudinal Behavioral Signals
New York University（纽约大学）；NYU Langone Health（NYU Langone健康）；NYU Grossman School of Medicine（NYU Grossman医学院）；Oregon Health & Science University（俄勒冈健康与科学大学）；Columbia University（哥伦比亚大学）；Harvard Medical School（哈佛医学院）
