Data pruning, which aims to downsize a large training set into a small informative subset, is crucial for reducing the enormous computational costs of modern deep learning. Though large-scale data collections invariably contain annotation noise and numerous robust learning methods have been developed, data pruning for the noise-robust learning scenario has received little attention. With state-of-the-art Re-labeling methods that self-correct erroneous labels while training, it is challenging to identify which subset induces the most accurate re-labeling of erroneous labels in the entire training set. In this paper, we formalize the problem of data pruning with re-labeling. We first show that the likelihood of a training example being correctly re-labeled is proportional to the prediction confidence of its neighborhood in the subset. Therefore, we propose a novel data pruning algorithm, Prune4Rel, that finds a subset maximizing the total neighborhood confidence of all training examples, thereby maximizing the re-labeling accuracy and generalization performance. Extensive experiments on four real and one synthetic noisy datasets show that \algname{} outperforms the baselines with Re-labeling models by up to 9.1% as well as those with a standard model by up to 21.6%.
Maximizing Efficiency of Dataset Compression for Machine Learning Potentials With Information Theory
Department of Materials Science and Engineering, University of California, Los Angeles, CA, United States（加州大学洛杉矶分校材料科学与工程系）；Materials Science Division, Lawrence Livermore National Laboratory, CA, United States（劳伦斯利弗莫尔国家实验室材料科学部）
Rotation-Robust Regression with Convolutional Model Trees
Department of Automation, Harbin Institute of Technology, Shenzhen, China（自动化系，哈尔滨工业大学，深圳）；Department of Computing Science, University of Alberta, Edmonton, Canada（计算机科学系，阿尔伯塔大学，爱德蒙顿）
PrimeSVT: An Automated Memory-aware Pruning Framework with Prioritized Compression Policy for Spiking Vision Transformers
eBRAIN Lab, Division of Engineering, New York University (NYU) Abu Dhabi（eBRAIN实验室，工程系，纽约大学（NYU）阿布扎克分校）；New York University (NYU) Abu Dhabi, United Arab Emirates (UAE)（纽约大学（NYU）阿布扎克分校，阿拉伯联合酋长国（UAE））
Pruning-aware Loss Functions for STOI-Optimized Pruned Recurrent Autoencoders for the Compression of the Stimulation Patterns of Cochlear Implants at Zero Delay
Model Pruning Enables Efficient Federated Learning on Edge Devices
Yale University（耶鲁大学）；IBM T. J. Watson Research Center（IBM托马斯·J·沃森研究中心）；Stanford Institute for Human-Centered Artificial Intelligence（斯坦福人类中心人工智能研究所）；Imperial College London（帝国理工学院）
Self-Training of Halfspaces with Generalization Guarantees under Massart Mislabeling Noise Model
Computer Science Laboratory (LIG)（计算机科学实验室（LIG））；Department of Statistics (LJK)（统计系（LJK））；Departement of Physics (SIMaP)（物理系（SIMaP））；University of Grenoble Alpes（格勒诺布尔阿尔卑斯大学）
