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cs.LG2021
Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts
Yujiang He
Compared with traditional deep learning techniques, continual learning enables deep neural networks to learn continually and adaptively. Deep neural networks have to learn new task…
cs.LG2019★ 2 cited
Regression via Arbitrary Quantile Modeling
Faen Zhang, Xinyu Fan, Hui Xu +3
In the regression problem, L1 and L2 are the most commonly used loss functions, which produce mean predictions with different biases. However, the predictions are neither robust no…
cs.LG2019
Dynamic Connected Neural Decision Classifier and Regressor with Dynamic Softing Pruning
Xinyu Fan
To deal with various datasets over different complexity, this paper presents an self-adaptive learning model that combines the proposed Dynamic Connected Neural Decision Networks (…