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2024 ICML ICML 2024

Seesaw: Compensating for Nonlinear Reduction with Linear Computations for Private Inference

Abstract

No abstract available. No abstract was captured for this paper when its proceedings were scraped - most often an older edition published without one online. How the corpus is built.

Authors

Fabing Li, Yuanhao Zhai, Shuangyu Cai, Mingyu Gao

Topics

Machine Learning > Application Areas > Efficient Computing Deep Learning > Techniques > Model Architecture Deep Learning > Optimization & Theory > Efficient Computing

Keywords

privacy-preserving machine learning neural architecture search linear computations nonlinear reduction online latency total latency
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