Modern Hopfield energy identifies high-energy samples as more prone to intrinsic forgetting in continual learning, with effective energy-based replay validated in diffusion models.
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2026 2verdicts
UNVERDICTED 2representative citing papers
A generative adversarial network sampling technique is proposed to estimate fault tolerance of failure modes in digital circuits by comparing expected and realistic signals.
citing papers explorer
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Continual Learning in Modern Hopfield Networks with an Application to Diffusion Models
Modern Hopfield energy identifies high-energy samples as more prone to intrinsic forgetting in continual learning, with effective energy-based replay validated in diffusion models.
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Fault tolerance estimation in digital circuits with visualised generative networks
A generative adversarial network sampling technique is proposed to estimate fault tolerance of failure modes in digital circuits by comparing expected and realistic signals.