Search Results for author:
Found 6 papers, 5 papers with code
Leveraging the Cell Ontology to classify unseen cell types
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
In this paper, we present a comprehensive empirical study on graph embedded few-shot learning.
Open-World Semi-Supervised Learning
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.
Ranked #1 on
Open-World Semi-Supervised Learning
on ImageNet-100
Concept Learners for Few-Shot Learning
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.
Selective Sampling-based Scalable Sparse Subspace Clustering
Sparse subspace clustering (SSC) represents each data point as a sparse linear combination of other data points in the dataset.
Multi-view Low-rank Sparse Subspace Clustering
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.
