PRISM learns shared sentiment prototypes to enable structured cross-modal comparison and dynamic modality reweighting in multimodal sentiment analysis, outperforming baselines on three benchmark datasets.
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A new optimization approach for HDC hypervectors minimizes distortion from hardware nonlinearities, delivering up to 48% higher accuracy on QuantHD and 5.4x gains on RelHD under severe perturbations.
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Learning Shared Sentiment Prototypes for Adaptive Multimodal Sentiment Analysis
PRISM learns shared sentiment prototypes to enable structured cross-modal comparison and dynamic modality reweighting in multimodal sentiment analysis, outperforming baselines on three benchmark datasets.
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Robust Reasoning and Learning with Brain-Inspired Representations under Hardware-Induced Nonlinearities
A new optimization approach for HDC hypervectors minimizes distortion from hardware nonlinearities, delivering up to 48% higher accuracy on QuantHD and 5.4x gains on RelHD under severe perturbations.