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09/08/2023
Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning
Real-world graphs naturally exhibit hierarchical or cyclical structures ...
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09/08/2023
3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation
Pretraining molecular representations from large unlabeled data is essen...
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01/27/2023
Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers
Since the recent advent of regulations for data protection (e.g., the Ge...
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10/27/2022
Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost
To overcome the quadratic cost of self-attention, recent works have prop...
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09/26/2022
Improving Multi-fidelity Optimization with a Recurring Learning Rate for Hyperparameter Tuning
Despite the evolution of Convolutional Neural Networks (CNNs), their per...
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09/07/2022
Grouping-matrix based Graph Pooling with Adaptive Number of Clusters
Graph pooling is a crucial operation for encoding hierarchical structure...
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08/22/2022
Equivariant Hypergraph Neural Networks
Many problems in computer vision and machine learning can be cast as lea...
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07/06/2022
Pure Transformers are Powerful Graph Learners
We show that standard Transformers without graph-specific modifications ...
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11/12/2021