首页|Findings from University of California Irvine Provide New Insights into Machine Learning (Dynafuse: Dynamic Fusion for Resource Efficient Multimodal Machine Learning Inference)
Findings from University of California Irvine Provide New Insights into Machine Learning (Dynafuse: Dynamic Fusion for Resource Efficient Multimodal Machine Learning Inference)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators publish new report on Machine Learning. According to news reportingoriginating in Irvine, California, by NewsRx journalists, research stated, “Multimodal machine learning(MMML) applications combine results from different modalities in the inference phase to improve predictionaccuracy. Existing MMML fusion strategies use static modality weight assignment, based on the intrinsicvalue of sensor modalities determined during the training phase.”
IrvineCaliforniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversity of California Irvine
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