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Representation-Constrained Autoencoders and an Application to Wireless Positioning
In a number of practical applications that rely on dimensionality reduction, the dataset or measurement process provides valuable side information that can be incorporated when learning low-dimensional embeddings.
WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic
Low-precision neural networks represent both weights and activations with few bits, drastically reducing the multiplication complexity.
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