Skip To Main Content
Min-ling Zhang on Muck Rack

Min-ling Zhang

As seen in: arXiv
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with Min-ling

Contact Min-ling, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

[2502.10184] Realistic Evaluation of Deep Partial-Label Learning Algorithms

|
By Dong-Dong Wu, Jindong Wang, Gang Niu, Min-ling Zhang
| arXiv
Open in Who Shared Issue with article?

EAT: Towards Long-Tailed Out-of-Distribution Detection

|
By Min-ling Zhang, Bo-Lin Wang
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (693) Issue with article?

[2305.09897] Complementary Classifier Induced Partial Label Learning

|
By Chongjie Si, Min-ling Zhang
| arXiv
In partial label learning (PLL), each training sample is associated with a set of candidate labels, among which only one is valid. The core of PLL is to disambiguate the candidate labels to get the ground-truth one. In disambiguation, the existing works usually do not fully investigate the effectiveness of the non-candidate label set (a.k.a.
Open in Who Shared Issue with article?
See all 3 articles