Transfer learningIn this work, we study the transfer learning problem under high-dimensional generalized linear models (GLMs), which aim to improve the fit on target data by borrowing information from useful source data. Given which sources to transfer, we propose a transfer learning algorithm on ...
Li, S., Cai, T. T., & Li, H. (2022). Transfer learning for high-dimensional linear regression: Prediction, estimation and minimax optimality. Journal of the Royal Statistical Society Series B: Statistical Methodology, 84, 149–173. Zhao J, Zheng S, Leng C. Residual Importance Weighted T...
把Trans-Lasso算法拓展到了Functional Linear Regression的情况。Structural Interpretability是指考虑functional space 是Reproducing Kernel Hilbert Spaces(RKHS)的情况。使得在其上的量化相似度和distance是可以用RKHS自身的性质解释的。 Residual Importance Weighted Transfer Learning For High-dimensional Linear Regression[J...
Transfer learning is amachine learningapproach that involves utilizing knowledge acquired from one task to improve performance on a different but related task. For example, if we train a model to recognize backpacks in pictures, we can use it to identify objects like sunglasses, a cap or a tabl...
We find that the mapping from gene expression patterns to cell types can be learnt using a simple generalized linear model (here, MLR) that is easily interpretable. This learning is only marginally improved by more complex artificial neural networks, which suffer from poor interpretability. Our res...
(Fig.3). We have observed through experimental validation that the ANN outperforms linear regression model when used on batteries of unseen devices. Additionally, transfer learning is possible using ANNs, where, the model generalizes on new data while retaining its earlier learning. The ANN has ...
In contrast to the other inductive transfer learning methods, the method uses the generalized linear model such that it becomes simpler and more interpretable. Experimental results indicate the effectiveness of the proposed method for multiclass epileptic EEG signal recognition. 展开 ...
The first work on causal transfer learning 日本理论组大佬Sugiyama的工作,causal transfer learning 20191008 CVPR-19 Characterizing and Avoiding Negative Transfer Characterizing and avoid negative transfer 形式化并提出如何避免负迁移 20190301 ALT-19 A Generalized Neyman-Pearson Criterion for Optimal Dom...
s learning rate settings, you should save and reload your model beforehand to get back to the state it was in before you called find_lr() and also reinitialize the optimizer you’ve chosen, which you can do now, passing in the learning rate you’ve determined from looking at the graph...
Transfer learning is designed to leverage knowledge in the source domain with labels to help build classification models in the target domain where labels