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Found 6 papers, 3 papers with code
QGen: On the Ability to Generalize in Quantization Aware Training
In this work, we investigate the generalization properties of quantized neural networks, a characteristic that has received little attention despite its implications on model performance.
Feedback-guided Data Synthesis for Imbalanced Classification
In this work, we introduce a framework for augmenting static datasets with useful synthetic samples, which leverages one-shot feedback from the classifier to drive the sampling of the generative model.
QReg: On Regularization Effects of Quantization
To confirm our analytical study, we performed an extensive list of experiments summarized in this paper in which we show that the regularization effects of quantization can be seen in various vision tasks and models, over various datasets.
LEAD: Min-Max Optimization from a Physical Perspective
A central obstacle in the optimization of such games is the rotational dynamics that hinder their convergence.
Negative Momentum for Improved Game Dynamics
Games generalize the single-objective optimization paradigm by introducing different objective functions for different players.
SLA Violation Prediction In Cloud Computing: A Machine Learning Perspective
Service level agreement (SLA) is an essential part of cloud systems to ensure maximum availability of services for customers.
