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AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation
In the domain of streaming recommender systems, conventional methods for addressing new user IDs or item IDs typically involve assigning initial ID embeddings randomly.
Stabilizing Training of Generative Adversarial Nets via Langevin Stein Variational Gradient Descent
Generative adversarial networks (GANs), famous for the capability of learning complex underlying data distribution, are however known to be tricky in the training process, which would probably result in mode collapse or performance deterioration.
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