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Found 8 papers, 0 papers with code
Supervised Gradual Machine Learning for Aspect Category Detection
Aspect Category Detection (ACD) aims to identify implicit and explicit aspects in a given review sentence.
Few-shot Image Classification based on Gradual Machine Learning
Specifically, our proposed solution extracts indicative feature representations by deep backbones, and then constructs both unary and binary factors based on the extracted features to facilitate gradual learning.
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.
Gradual Machine Learning for Aspect-level Sentiment Analysis
The state-of-the-art solutions for Aspect-Level Sentiment Analysis (ALSA) were built on a variety of deep neural networks (DNN), whose efficacy depends on large amounts of accurately labeled training data.
Gradual Machine Learning for Entity Resolution
Using ER as a test case, we demonstrate that gradual machine learning is a promising paradigm potentially applicable to other challenging classification tasks requiring extensive labeling effort.
