Search Results for author: Kelvin K. W. Ng

Found 3 papers, 2 papers with code

Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search

2 code implementations12 Nov 2019 Xinyan Dai, Xiao Yan, Kelvin K. W. Ng, Jie Liu, James Cheng

In this paper, we present a new angle to analyze the quantization error, which decomposes the quantization error into norm error and direction error.

Data Compression Quantization

Hyper-Sphere Quantization: Communication-Efficient SGD for Federated Learning

1 code implementation12 Nov 2019 Xinyan Dai, Xiao Yan, Kaiwen Zhou, Han Yang, Kelvin K. W. Ng, James Cheng, Yu Fan

In particular, at the high compression ratio end, HSQ provides a low per-iteration communication cost of $O(\log d)$, which is favorable for federated learning.

Federated Learning Quantization

Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization

no code implementations26 Feb 2018 Fanhua Shang, Yuanyuan Liu, Kaiwen Zhou, James Cheng, Kelvin K. W. Ng, Yuichi Yoshida

In order to make sufficient decrease for stochastic optimization, we design a new sufficient decrease criterion, which yields sufficient decrease versions of stochastic variance reduction algorithms such as SVRG-SD and SAGA-SD as a byproduct.

Stochastic Optimization

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