Using sequences of life-events to predict human lives

G Savcisens, T Eliassi-Rad, LK Hansen… - Nature Computational …, 2024 - nature.com
Here we represent human lives in a way that shares structural similarity to language, and we
exploit this similarity to adapt natural language processing techniques to examine the …

Positive-unlabeled learning to infer protection status and identify correlates in vaccine efficacy field trials

S Xu, NS Kelkar, ME Ackerman - iScience, 2024 - cell.com
Correlates of protection (CoPs) are key guideposts that both support vaccine development
and licensure as well as improve our understanding of the attributes of immune responses …

AIS-based profiling of fishing vessels falls short as a “proof of concept” for identifying forced labor at sea

W Swartz, AM Cisneros-Montemayor… - Proceedings of the …, 2021 - National Acad Sciences
McDonald et al.(1) argue that labor conditions in fisheries can be discerned from the
movement and characteristics of fishing vessels. We recognize the authors' effort, yet have …

Automatic identification of teachers in social media using positive unlabeled learning

H Karimi, J Tang, X Weiss… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
With the emergence of online social media platforms, there has been a surge of
teachers/educators turning to these platforms for professional purposes, eg, supplementing …

[图书][B] Teachers in social media: a data science perspective

H Karimi - 2021 - search.proquest.com
Social media has become an integral part of human life in the 21st century. The number of
social media users was estimated to be around 3.6 billion individuals in 2020. Social media …

A hybrid positive unlabeled learning framework for uncovering scaffolds across human proteome by measuring the propensity to drive phase separation

P Jiang, R Cai, J Lugo-Martinez… - Briefings in …, 2023 - academic.oup.com
Scaffold proteins drive liquid–liquid phase separation (LLPS) to form biomolecular
condensates and organize various biochemical reactions in cells. Dysregulation of scaffolds …

[PDF][PDF] The Hidden Cost of Fraud: An Instance-Dependent Cost-Sensitive Approach for Positive and Unlabeled Learning

CO Vasquez, J De Weerdt… - … on Learning with …, 2022 - proceedings.mlr.press
Financial institutions have increasingly suffered pressure to implement better and faster
fraud detection systems to minimize the cost of fraud. This issue has attracted attention from …

Case-related news filtering via topic-enhanced positive-unlabeled learning

G Wang, Z Yu, Y Xian, Y Zhang - Journal of Information Processing …, 2021 - koreascience.kr
Case-related news filtering is crucial in legal text mining and divides news into case-related
and case-unrelated categories. Because case-related news originates from various fields …

[PDF][PDF] Self-supervised Learning Framework for Imbalanced Positive-Unlabeled Data

J Schweisthal - 2022 - epub.ub.uni-muenchen.de
Abstract Positive Unlabeled (PU) Learning is a binary classification problem where only
positive and unlabeled data are available. Most methods are designed for balanced …

Deep learning and semi-supervised methods for addressing learning with unlabeled data in telco package recommendation models

LPS Santos - 2023 - repositorio.ul.pt
Recommender systems have seen an increasing adoption across many industries that aim
in achieving a better relationship with the customer and increase their profits. Hence, the …