Search Results for author:
Found 4 papers, 1 papers with code
New Insights on Relieving Task-Recency Bias for Online Class Incremental Learning
In all settings, the online class incremental learning (OCIL), where incoming samples from data stream can be used only once, is more challenging and can be encountered more frequently in real world.
Active Deep Learning on Entity Resolution by Risk Sampling
Building upon the recent advances in risk analysis for ER, which can provide a more refined estimate on label misprediction risk than the simpler classifier outputs, we propose a novel AL approach of risk sampling for ER.
Adaptive Deep Learning for Entity Resolution by Risk Analysis
Built on the recent advances on risk analysis for ER, the proposed approach first trains a deep model on labeled training data, and then fine-tunes it by minimizing its estimated misprediction risk on unlabeled target data.
Towards Interpretable and Learnable Risk Analysis for Entity Resolution
Machine-learning-based entity resolution has been widely studied.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
