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The Chinese University of Hong Kong
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Achieving differential privacy in multi-objective submodular maximization opens new avenues for handling sensitive data without sacrificing performance.
Recurrent memory can be added to transformers at scale with minimal parameter overhead and no performance penalty by reusing existing hidden states and training with interleaved parallel updates.
