A framework using measured noise proxies, chance-constrained training, and noise-aware LayerNorm enables Vision Transformers to achieve near-clean accuracy on noisy microring-resonator photonic arrays without in-situ learning or added optical operations.
Weight uncertainty in neural network
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
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DALorRA applies variational Bayesian sparse masking to LoRA ranks to calibrate LLM uncertainty while preserving accuracy.
citing papers explorer
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Light-Bound Transformers: Hardware-Anchored Robustness for Silicon-Photonic Computer Vision Systems
A framework using measured noise proxies, chance-constrained training, and noise-aware LayerNorm enables Vision Transformers to achieve near-clean accuracy on noisy microring-resonator photonic arrays without in-situ learning or added optical operations.
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Bayesian Sparse Low-Rank Adaptation for Large Language Model Uncertainty Estimation
DALorRA applies variational Bayesian sparse masking to LoRA ranks to calibrate LLM uncertainty while preserving accuracy.