Rank-based Decomposable Losses in Machine Learning: A Survey-Reference-Cited by-同舟云学术

Rank-based Decomposable Losses in Machine Learning: A Survey

Author:

Hu Shu¹^ORCID,Wang Xin²^ORCID,Lyu Siwei²^ORCID

Affiliation:

1. Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA

2. Department of Computer Science and Engineering, University at Buffalo, SUNY, Buffalo, NY, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

Reference210 articles.

1. The elements of statistical learning: data mining, inference, and prediction;hastie,2009

2. Not all samples are created equal: Deep learning with importance sampling;katharopoulos;Proc Int Conf Mach Learn,2018

4. Training deep models faster with robust, approximate importance sampling;johnson;Proc Int Conf Neural Inf Process,2018

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Masked Conditional Diffusion Model for Enhancing Deepfake Detection;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

2. Uncertainty-Aware Explainable Recommendation with Large Language Models;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30