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Found 2 papers, 1 papers with code
Learning to map between ferns with differentiable binary embedding networks
Current deep learning methods are based on the repeated, expensive application of convolutions with parameter-intensive weight matrices.
TernaryNet: Faster Deep Model Inference without GPUs for Medical 3D Segmentation using Sparse and Binary Convolutions
We propose a new scheme that approximates both trainable weights and neural activations in deep networks by ternary values and tackles the open question of backpropagation when dealing with non-differentiable functions.
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