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Found 5 papers, 0 papers with code
Geometrically Aligned Transfer Encoder for Inductive Transfer in Regression Tasks
Transfer learning is a crucial technique for handling a small amount of data that is potentially related to other abundant data.
3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation
Pretraining molecular representations from large unlabeled data is essential for molecular property prediction due to the high cost of obtaining ground-truth labels.
Grouping-matrix based Graph Pooling with Adaptive Number of Clusters
Conventional methods ask users to specify an appropriate number of clusters as a hyperparameter, then assume that all input graphs share the same number of clusters.
Semi-supervised regression with skewed data via adversarially forcing the distribution of predicted values
We assume the existence of enough unlabeled data that follow the true distribution, and that the true distribution can be roughly estimated from domain knowledge or a few samples.
Effective Network Compression Using Simulation-Guided Iterative Pruning
Existing high-performance deep learning models require very intensive computing.
