490 citations · 639 across the 6 of their papers we have counts for
6 papers
Cross-domain Open-world Discovery
Shuo Wen, Maria Brbic
In many real-world applications, test data may commonly exhibit categorical shifts, characterized by the emergence of novel classes, as well as distribution shifts arising from fea…
Model-Agnostic Graph Regularization for Few-Shot Learning
Ethan Shen, Maria Brbic, Nicholas Monath +3
In many domains, relationships between categories are encoded in the knowledge graph. Recently, promising results have been achieved by incorporating knowledge graph as side inform…
Open-World Semi-Supervised Learning
Kaidi Cao, Maria Brbic, Jure Leskovec
A fundamental limitation of applying semi-supervised learning in real-world settings is the assumption that unlabeled test data contains only classes previously encountered in the…
Concept Learners for Few-Shot Learning
Kaidi Cao, Maria Brbic, Jure Leskovec
Developing algorithms that are able to generalize to a novel task given only a few labeled examples represents a fundamental challenge in closing the gap between machine- and human…
-Motivated Low-Rank Sparse Subspace Clustering
Maria Brbić, Ivica Kopriva
In many applications, high-dimensional data points can be well represented by low-dimensional subspaces. To identify the subspaces, it is important to capture a global and local st…
Multi-view Low-rank Sparse Subspace Clustering
Maria Brbic, Ivica Kopriva
Most existing approaches address multi-view subspace clustering problem by constructing the affinity matrix on each view separately and afterwards propose how to extend spectral cl…