17 citations · 73 across the 11 of their papers we have counts for
6 papers · 1 filter
Deep Learning From Crowdsourced Labels: Coupled Cross-entropy Minimization, Identifiability, and Regularization
Shahana Ibrahim, Tri Nguyen, Xiao Fu
Using noisy crowdsourced labels from multiple annotators, a deep learning-based end-to-end (E2E) system aims to learn the label correction mechanism and the neural classifier simul…
Under-Counted Tensor Completion with Neural Incorporation of Attributes
Shahana Ibrahim, Xiao Fu, Rebecca Hutchinson +1
Systematic under-counting effects are observed in data collected across many disciplines, e.g., epidemiology and ecology. Under-counted tensor completion (UC-TC) is well-motivated…
Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach
Tri Nguyen, Shahana Ibrahim, Xiao Fu
The recent integration of deep learning and pairwise similarity annotation-based constrained clustering -- i.e., (DCC) -- has proven effectiv…
Crowdsourcing via Annotator Co-occurrence Imputation and Provable Symmetric Nonnegative Matrix Factorization
Shahana Ibrahim, Xiao Fu
Unsupervised learning of the Dawid-Skene (D&S) model from noisy, incomplete and crowdsourced annotations has been a long-standing challenge, and is a critical step towards reliably…
Mixed Membership Graph Clustering via Systematic Edge Query
Shahana Ibrahim, Xiao Fu
This work considers clustering nodes of a largely incomplete graph. Under the problem setting, only a small amount of queries about the edges can be made, but the entire graph is n…
Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms
Shahana Ibrahim, Xiao Fu, Nikos Kargas +1
The data deluge comes with high demands for data labeling. Crowdsourcing (or, more generally, ensemble learning) techniques aim to produce accurate labels via integrating noisy, no…