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Found 6 papers, 3 papers with code
Learning Graph Augmentations to Learn Graph Representations
Devising augmentations for graph contrastive learning is challenging due to their irregular structure, drastic distribution shifts, and nonequivalent feature spaces across datasets.
Group-disentangled Representation Learning with Weakly-Supervised Regularization
Learning interpretable and human-controllable representations that uncover factors of variation in data remains an ongoing key challenge in representation learning.
Learning Task-Relevant Features via Contrastive Input Morphing
A fundamental challenge in artificial intelligence is learning useful representations of data that yield good performance on a downstream classification task, without overfitting to spurious input features.
PointMask: Towards Interpretable and Bias-Resilient Point Cloud Processing
We show that coupling a PointMask layer with an arbitrary model can discern the points in the input space which contribute the most to the prediction score, thereby leading to interpretability.
Contrastive Multi-View Representation Learning on Graphs
We achieve new state-of-the-art results in self-supervised learning on 8 out of 8 node and graph classification benchmarks under the linear evaluation protocol.
Memory-Based Graph Networks
Graph neural networks (GNNs) are a class of deep models that operate on data with arbitrary topology represented as graphs.
