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Found 4 papers, 2 papers with code
SPFQ: A Stochastic Algorithm and Its Error Analysis for Neural Network Quantization
Quantization is a widely used compression method that effectively reduces redundancies in over-parameterized neural networks.
Post-training Quantization for Neural Networks with Provable Guarantees
Additionally, our error analysis expands the results of previous work on GPFQ to handle general quantization alphabets, showing that for quantizing a single-layer network, the relative square error essentially decays linearly in the number of weights -- i. e., level of over-parametrization.
Sigma-Delta and Distributed Noise-Shaping Quantization Methods for Random Fourier Features
We propose the use of low bit-depth Sigma-Delta and distributed noise-shaping methods for quantizing the Random Fourier features (RFFs) associated with shift-invariant kernels.
Faster Binary Embeddings for Preserving Euclidean Distances
When $\mathcal{T}$ consists of well-spread (i. e., non-sparse) vectors, our embedding method applies a stable noise-shaping quantization scheme to $A x$ where $A\in\mathbb{R}^{m\times n}$ is a sparse Gaussian random matrix.
