Theoretical analysis reveals MaskGIT's implicit temperature sampling in masked diffusion; proposes equivalent moment sampler and efficiency techniques for adaptive unmasking with image and text experiments.
Overcoming dimensional factorization limits in discrete diffusion models through quantum joint distribution learning.arXiv preprint arXiv:2505.05151,
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Quantum neural networks exhibit membership privacy leakage that a proposed quantum machine unlearning framework with three mechanisms can mitigate in simulations and cloud device tests.
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Demystifying MaskGIT Sampler and Beyond: Adaptive Order Selection in Masked Diffusion
Theoretical analysis reveals MaskGIT's implicit temperature sampling in masked diffusion; proposes equivalent moment sampler and efficiency techniques for adaptive unmasking with image and text experiments.
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From Membership-Privacy Leakage to Quantum Machine Unlearning
Quantum neural networks exhibit membership privacy leakage that a proposed quantum machine unlearning framework with three mechanisms can mitigate in simulations and cloud device tests.