[Workshop description]
Machine learning should not be accessible only to those who can pay. Specifically, modern machine learning is migrating to the era of complex models (e.g., deep neural networks), which require a plethora of well-annotated data. Giant companies have enough money to collect well-annotated data. However, for startups or non-profit organizations, such data is barely acquirable due to the cost of labeling data or the intrinsic scarcity in the given domain. These practical issues motivate us to research and pay attention to weakly supervised learning (WSL), since WSL does not require such a huge amount of annotated data. We define WSL as the collection of machine learning problem settings and algorithms that share the same goals as supervised learning but can only access to less supervised information than supervised learning. In this workshop, we discuss both theoretical and applied aspects of WSL.
This workshop is a series of the previous workshops at ACML 2019, SDM 2020, ACML 2020, and IJCAI 2021.
Our particular emphasis at this workshop is incomplete supervision, inexact supervision, inaccurate supervision, cross-domain supervision, imperfect demonstration, and weak adversarial supervision (new topic).
[Workshop link] <https://wsl-workshop.github.io/acml21.html>
Topic: ACML2021 Weakly Supervised Learning Workshop
Time: Nov 17, 2021 09:30 AM Bangkok
[Timeline]
9:30 am - 12:10pm, Nov. 17th, 2021 (GMT+7)
*Corresponds to Beijing/Singapore Time from 10:30am - 1:10pm;
*Corresponds to Tokyo Time from 11:30 am - 2:10pm;
*Corresponds to Los Angeles Time (previous day) from 7:30pm-10:10pm.
[Highlight]
We have confirmed three well-known speakers for contributing to keynote talks.
Zhouchen Lin, Peking University, China
Hanwang Zhang, Nanyang Technological University, Singapore
Boqing Gong, Research Scientist Google, US
We sincerely welcome anyone to participate in this workshop.
[Contact]
Please contact jingfen...@riken.jp if you have any inquiries.
Thanks,
ACML2021 WSL workshop team