mutual information
mutual-information-122d67cc·2 events·first seen 1mo agoAliases: mutual information
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Information-Driven Design of Imaging Systems
Researchers from Berkeley present a framework for evaluating and optimizing imaging systems based on mutual information content rather than traditional metrics like resolution or SNR, published at NeurIPS 2025. The method estimates mutual information directly from noisy measurements using known noise physics and learned probabilistic models (including transformers and PixelCNN), avoiding the need for task-specific decoders. Validated across four domains—color photography, radio astronomy, lensless imaging, and microscopy—the information metric predicts downstream decoder performance and enables hardware optimization with less compute and memory than end-to-end neural approaches.
Active Query Synthesis for Preference Learning via Mutual Information Maximization
This paper introduces Info-Synth, an active query synthesis framework for preference learning that generates optimal pairwise queries by maximizing a mutual information objective in continuous space, bypassing the computational cost of pool-based evaluation. A confidence-aware response model is proposed to handle ambiguous comparisons between nearly identical or highly dissimilar items. Two finite-pool extensions (Pair M-dist and Pair Opt-dist) are also introduced. The framework is validated on synthetic preference tasks, text summarization datasets, and robotic controller tuning.