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- W2143808534 abstract "We present the bump mixture model, a statistical model for analog data where the probabilistic semantics, inference, and learning rules derive from low-level transistor behavior. The bump mixture model relies on translinear circuits to perform probabilistic inference, and floating-gate devices to perform adaptation. This system is low power, asynchronous, and fully parallel, and supports various on-chip learning algorithms. In addition, the mixture model can perform several tasks such as probability estimation, vector quantization, classification, and clustering. We tested a fabricated system on clustering, quantization, and classification of handwritten digits and show performance comparable to the E-M algorithm on mixtures of Gaussians." @default.
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- W2143808534 date "2002-01-01" @default.
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- W2143808534 title "Adaptive Quantization and Density Estimation in Silicon" @default.
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