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Research questionHow can low-precision recurrent-state storage preserve small updates during temporal inference?Recurrent networks repeatedly store and reuse their internal state, so coarse write-back can discard updates that fall below the storage threshold. Over many time steps, these omissions can distort estimates from noisy temporal signals.
Image & Video Processing
Inference Optimization
Machine Learning
Neural and Evolutionary Computing
Latest papersRecent research connected to this question, newest first.When Quantization Breaks Memory: Recurrent-State Write-Back in Low-Precision Temporal InferenceThe evidence comes from compact GRU and independently trained LSTM encoder-decoder models for fluorescence lifetime imaging, estimating short- and long-lived components from high-noise time-resolved fluorescence signals. It examines deterministic 4-bit post-training state storage, state-specific interventions, error feedback, residual memory, direction memory, and matched training; the reported findings do not establish behavior across other architectures or tasks.research paper · Sep 3, 2026
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