In this work, we ask two key questions. First, in a more realistic setting where only a CPU is available, is the GNN model still competitive?

Bob) update her actions in the odd rounds so that, regardless of Bob's

Bob) update her actions in the odd rounds so that, regardless of Bob's

Hence, extensiveresearch has been proposed to improvethisbound through richer distributions [55,53,36,13].

Autoencoders (AEs) [1, 2] and its modern variants like the widely used variational autoencoders (VAEs) [3], are a powerful paradigm for self-supervised representation learning for generative modeling [4], compression [5], anomaly detection [6] or natural language processing [7]. Since autoencoders can learn low dimensional representations without requiring labeled data, they are particularly useful forcomputer vision taskswhere samples canbeveryhighdimensional making processing, transmitting, and search prohibitively expensive.

The Probabilistic Watershed isasemi-supervised learning algorithm applied on undirected graphs.