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Found 3 papers, 1 papers with code
Trustworthy Representation Learning Across Domains
Inspired by the concepts in trustworthy AI, we proposed the first trustworthy representation learning across domains framework which includes four concepts, i. e, robustness, privacy, fairness, and explainability, to give a comprehensive literature review on this research direction.
Better Generative Replay for Continual Federated Learning
Federated learning is a technique that enables a centralized server to learn from distributed clients via communications without accessing the client local data.
Less is More: Data-Efficient Complex Question Answering over Knowledge Bases
Our framework consists of a neural generator and a symbolic executor that, respectively, transforms a natural-language question into a sequence of primitive actions, and executes them over the knowledge base to compute the answer.
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