Gradient-based optimization learns symmetric Gaussian mixture modes for 2-bit fixed-point weight quantization, claiming state-of-the-art performance and self-adaptive weights.
Explicit loss-error-aware quantization for low-bit deep neural networks
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Learning Multimodal Fixed-Point Weights using Gradient Descent
Gradient-based optimization learns symmetric Gaussian mixture modes for 2-bit fixed-point weight quantization, claiming state-of-the-art performance and self-adaptive weights.