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The Pursuit of Human Labeling: A New Perspective on Unsupervised Learning
Neural Information Processing Systems
However, zero-shot learning methods still require human instruction set to solve a new task.
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FedFed: Feature Distillation against Data Heterogeneity in Federated Learning Zhiqin Y ang
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Federated learning (FL) typically faces data heterogeneity, i.e., distribution shifting among clients. Sharing clients' information has shown great potentiality in mitigating data heterogeneity, yet incurs a dilemma in preserving privacy and promoting
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Robust Data Valuation with Weighted Banzhaf Values
Neural Information Processing Systems
We summarize our contributions as follows: 1.
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