Probabilistic HD-CB outperforms binarized HD-CB and approaches full HD-CB performance on synthetic benchmarks using as few as 3 bits per component via random partial updates with time-decaying probability.
Hyperdimensional computing: An introduction to comput- ing in distributed representation with high-dimensional random vectors,
2 Pith papers cite this work. Polarity classification is still indexing.
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AMS-HD applies hyperdimensional computing with mutual information feature selection and positional projection to match SVM and MLP accuracy for AMS detection while using far less power, memory, and hardware resources on FPGA and mobile platforms.
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Contextual Bandits for Resource-Constrained Devices using Probabilistic Learning
Probabilistic HD-CB outperforms binarized HD-CB and approaches full HD-CB performance on synthetic benchmarks using as few as 3 bits per component via random partial updates with time-decaying probability.
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AMS-HD: Hyperdimensional Computing for Real-Time and Energy-Efficient Acute Mountain Sickness Detection
AMS-HD applies hyperdimensional computing with mutual information feature selection and positional projection to match SVM and MLP accuracy for AMS detection while using far less power, memory, and hardware resources on FPGA and mobile platforms.