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Knowledge-Reuse Transfer Learning Methods in Molecular and Material Science
We focus on the application of transfer learning methods for the discovery of advanced molecules/materials, particularly, the construction of transfer learning frameworks for different systems, and how transfer learning can enhance the performance of models.
DeepRLI: A Multi-objective Framework for Universal Protein--Ligand Interaction Prediction
In order to equip the model to generalize to conformations beyond the confines of crystal structures and to adapt to molecular docking and virtual screening tasks, we propose a multi-objective strategy, that is, the model outputs three scores for scoring and ranking, docking, and screening, and the training process optimizes these three objectives simultaneously.
CILIATE: Towards Fairer Class-based Incremental Learning by Dataset and Training Refinement
The common practice leverages incremental learning (IL), e. g., Class-based Incremental Learning (CIL) that updates output labels, to update the model with new data and a limited number of old data.
Meta-Mining Discriminative Samples for Kinship Verification
In the end, the samples in the unbalanced train batch are re-weighted by the learned meta-miner to optimize the kinship models.
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Kinship Verification
on KinFaceW-II
