Sparse and Transferable Universal Singular Vectors Attack
Sber AI Lab（Sber AI实验室）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Artificial Intelligence Research Institute (AIRI)（人工智能研究院（AIRI））
The research in the field of adversarial attacks and models' vulnerability is one of the fundamental directions in modern machine learning. Recent studies reveal the vulnerability phenomenon, and understanding the mechanisms behind this is essential for improving neural network characteristics and interpretability. In this paper, we propose a novel sparse universal white-box adversarial attack. Our approach is based on truncated power iteration providing sparsity to $(p,q)$-singular vectors of the hidden layers of Jacobian matrices. Using the ImageNet benchmark validation subset, we analyze the proposed method in various settings, achieving results comparable to dense baselines with more than a 50% fooling rate while damaging only 5% of pixels and utilizing 256 samples for perturbation fitting. We also show that our algorithm admits higher attack magnitude without affecting the human ability to solve the task. Furthermore, we investigate that the constructed perturbations are highly transferable among different models without significantly decreasing the fooling rate. Our findings demonstrate the vulnerability of state-of-the-art models to sparse attacks and highlight the importance of developing robust machine learning systems.
Low-Cost High-Order Singular Value Decomposition for Tensor-Based Reconstruction from Sparse Sensor Measurements: Urban Flow and Air-Quality Applications
ETSI Aeronáutica y del Espacio, Universidad Politécnica de Madrid（马德里理工大学航空与航天工程学院）；Ove Arup（奥雅纳）；Department of Aerospace Engineering, University of Michigan（密歇根大学航空航天工程系）
Optimizing Sparse Generalized Singular Vectors for Feature Selection in Proximal Support Vector Machines with Application to Breast and Ovarian Cancer Detection
Tufts University（塔夫茨大学）；Colorado State University（科罗拉多州立大学）
Sparse Spectral Training and Inference on Euclidean and Hyperbolic Neural Networks
Center for Complex Network Intelligence (CCNI), Tsinghua Laboratory of Brain and Intelligence (THBI), Department of Psychological and Cognitive Sciences（复杂网络智能中心（CCNI）、清华脑智能实验室（THBI）、心理与认知科学系）；Department of Computer Science（计算机科学系）；Department of Biomedical Engineering, Tsinghua University, China（生物医学工程系，清华大学，中国）
