1. E(n) Equivariant Graph Neural Networks
2. Prototypical Contrastive Learning
3. Prototypical Networks for Few-shot Learning
4. Metric learning with adaptive density discrimination

Paper list:

1. Clustering with bregman divergences
2. Write a classifier: Zero-shot learning using purely textual descriptions
3. Distance-based image classification: Generalizing to new classes at near-zero cost
4. Metric learning with adaptive density discrimination
5. Matching networks for one shot learning
6. Deep clustering for unsupervised learning of visual features
7. Unsupervised learning of visual features by contrasting cluster assignments
8. Billion-scale similarity search with gpus
9. A theoretical analysis of contrastive unsupervised representation learning
10. Graph normalizing flows
11. Graphite: Iterative generative modeling of graphs
12. Neural relational inference for interacting systems
13. On the equivalence between positional node embeddings and structural graph representations
14. Set2graph: Learning graphs from sets