Loss Landscape Analysis for Reliable Quantized ML Models for Scientific Sensing

2025-02-12

AI Summary

Quantized machine learning models in scientific sensing exhibit insufficient robustness against noise and perturbations. Method: This paper proposes an empirical analytical framework grounded in the geometric properties of loss landscapes. It establishes, for the first time, a strong correlation between loss landscape smoothness and quantized model robustness—leveraging curvature analysis, quantization-aware perturbation modeling, and regularized comparative experiments—enabling prior assessment and Pareto-optimal trade-offs among accuracy, efficiency, and robustness without retraining. Results: Empirical evaluation demonstrates that smoother loss landscapes significantly enhance resilience to both input noise and quantization-induced degradation. The proposed method reduces robustness verification time by over 50%, providing scientific sensing systems with an interpretable, efficient, and adaptive foundation for quantized model deployment.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

In this paper, we propose a method to perform empirical analysis of the loss landscape of machine learning (ML) models. The method is applied to two ML models for scientific sensing, which necessitates quantization to be deployed and are subject to noise and perturbations due to experimental conditions. Our method allows assessing the robustness of ML models to such effects as a function of quantization precision and under different regularization techniques -- two crucial concerns that remained underexplored so far. By investigating the interplay between performance, efficiency, and robustness by means of loss landscape analysis, we both established a strong correlation between gently-shaped landscapes and robustness to input and weight perturbations and observed other intriguing and non-obvious phenomena. Our method allows a systematic exploration of such trade-offs a priori, i.e., without training and testing multiple models, leading to more efficient development workflows. This work also highlights the importance of incorporating robustness into the Pareto optimization of ML models, enabling more reliable and adaptive scientific sensing systems.

Problem

• Analyze loss landscape for quantized ML models
• Assess robustness to noise and perturbations
• Explore trade-offs in performance and efficiency

Innovation

• Loss landscape analysis
• Quantization precision assessment
• Robustness regularization techniques