1363591003's Stars
qingluM/CDAR
The source code for "Deep transfer learning for conditional shift in regression"
yaoyao-liu/meta-transfer-learning
TensorFlow and PyTorch implementation of "Meta-Transfer Learning for Few-Shot Learning" (CVPR2019)
leon-liangwu/MTL-PyTorch
PyTorch implementations for CVPR 2019 Paper "Meta-Transfer Learning for Few-Shot Learning"
jcj7292/MDA-CNN
Paper https://www.sciencedirect.com/science/article/pii/S0045782521007015
davidbuterez/multi-fidelity-gnns-for-drug-discovery-and-quantum-mechanics
Source code accompanying the 'Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting' paper
xw00616/Tr-SAEA
Little attention has been paid to more general and realistic optimization scenarios where different objectives are evaluated by different computer simulations or physical experiments with different time complexities and only a very limited number of function evaluations is allowed for the slow objective. We propose a transfer learning scheme within a surrogate-assisted evolutionary algorithm framework to augment the training data for the surrogate for the slow objective function by transferring knowledge from the fast one. Specifically, a hybrid domain adaptation method aligning the second-order statistics and marginal distributions across domains is introduced to generate promising samples in the decision space according to the search experience of the fast one. A Gaussian process model based co-training method is adopted to predict the value of the slow objective and those having a high confidence level are selected as the augmented synthetic training data, thereby enhancing the approximation quality of the surrogate of the slow objective.
AdelYogurt/Srgt_Optim
Surrogate model and surrogate base optimization algorithm
MaterialsInformaticsDemo/TCA
Domain Adaptation via Transfer Component Analysis
zhaochangming/BoostForest