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Found 7 papers, 1 papers with code
GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks
Graph neural networks (GNNs) learn the representation of nodes in a graph by aggregating the neighborhood information in various ways.
Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators
Multi-label learning is an emerging extension of the multi-class classification where an image contains multiple labels.
End-to-End Learning from Noisy Crowd to Supervised Machine Learning Models
Furthermore, relabeling only 10% of the data using the expert's results in over 90% classification accuracy with SVM.
Active Learning for Noisy Data Streams Using Weak and Strong Labelers
Labeling data correctly is an expensive and challenging task in machine learning, especially for on-line data streams.
TrustNet: Learning from Trusted Data Against (A)symmetric Label Noise
Based on the insights, we design TrustNet that first adversely learns the pattern of noise corruption, being it both symmetric or asymmetric, from a small set of trusted data.
QActor: On-line Active Learning for Noisy Labeled Stream Data
A central feature of QActor is to dynamically adjust the query limit according to the learning loss for each data batch.
Active Transfer Learning for Persian Offline Signature Verification
We benefit from transfer learning using a pre-trained CNN for feature learning.
