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This paper proposes a practical recipe for semi-supervised federated ASR using online pseudo-labels with server update stabilization, demonstrating significant improvements over prior methods in both in-domain and cross-domain settings.
This paper introduces Gaussian Bridge Consistency (GBC), a novel framework for long-tailed semi-supervised learning that uses Gaussian feature bridges to improve generalization and reduce confirmation bias, validated on benchmarks like CIFAR10-LT and ImageNet-LT.
The paper proposes DIF, a model-agnostic method for denoising implicit feedback in cold-start recommendation by using pseudo-labels from content-similar warm items and uncertainty estimation, achieving significant improvements in a billion-user video app.