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Found 2 papers, 2 papers with code
Unsupervised Sentence Representation Learning with Frequency-induced Adversarial Tuning and Incomplete Sentence Filtering
To solve the problems, we fine-tune PLMs by leveraging the frequency information of words and propose a novel USRL framework, namely Sentence Representation Learning with Frequency-induced Adversarial tuning and Incomplete sentence filtering (SLT-FAI).
Efficient Vertical Federated Learning Method for Ridge Regression of Large-Scale Samples via Least-Squares Solution
The experiments show that our proposed algorithm takes only about 400 seconds to handle up to 9. 6 million large-scale samples, while the state-of-the-art algorithms take close to 1000 seconds to handle every 1000 samples, which embodies the advantage of our algorithms in handling large-scale samples. δ -data indistinguishability theory, we provide quantitative theoretical guarantees for the security of our algorithms.
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