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Found 3 papers, 1 papers with code
Scalable Graph Self-Supervised Learning
Our experiments demonstrate that the cost associated with the loss computation can be reduced via node or dimension sampling without lowering the downstream performance.
WERank: Towards Rank Degradation Prevention for Self-Supervised Learning Using Weight Regularization
A common phenomena confining the representation quality in Self-Supervised Learning (SSL) is dimensional collapse (also known as rank degeneration), where the learned representations are mapped to a low dimensional subspace of the representation space.
Deep Policies for Online Bipartite Matching: A Reinforcement Learning Approach
We present an end-to-end Reinforcement Learning framework for deriving better matching policies based on trial-and-error on historical data.
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