Search Results for author: Maria Brbic

Found 6 papers, 5 papers with code

Leveraging the Cell Ontology to classify unseen cell types

1 code implementation Nature Communications 2021 Sheng Wang, Angela Oliveira Pisco, Aaron McGeever, Maria Brbic, Marinka Zitnik, Spyros Darmanis, Jure Leskovec, Jim Karkanias, Russ B. Altman

Single cell technologies are rapidly generating large amounts of data that enables us to understand biological systems at single-cell resolution.

Model-Agnostic Graph Regularization for Few-Shot Learning

no code implementations14 Feb 2021 Ethan Shen, Maria Brbic, Nicholas Monath, Jiaqi Zhai, Manzil Zaheer, Jure Leskovec

In this paper, we present a comprehensive empirical study on graph embedded few-shot learning.

Few-Shot Learning

Open-World Semi-Supervised Learning

1 code implementation ICLR 2022 Kaidi Cao, Maria Brbic, Jure Leskovec

Here, we introduce a novel open-world semi-supervised learning setting that formalizes the notion that novel classes may appear in the unlabeled test data.

Image Classification Open-World Semi-Supervised Learning

Concept Learners for Few-Shot Learning

1 code implementation ICLR 2021 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-level performance.

Few-Shot Learning Fine-Grained Image Classification

Selective Sampling-based Scalable Sparse Subspace Clustering

1 code implementation NeurIPS 2019 Shin Matsushima, Maria Brbic

Sparse subspace clustering (SSC) represents each data point as a sparse linear combination of other data points in the dataset.

Representation Learning

Multi-view Low-rank Sparse Subspace Clustering

2 code implementations29 Aug 2017 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 clustering algorithm to handle multi-view data.

Multi-view Subspace Clustering

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