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Related Concept Videos

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments.

Angie Boggust, Venkatesh Sivaraman, Yannick Assogba

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    Summary
    This summary is machine-generated.

    Practitioners often struggle to compare machine learning model compression experiments. The new COMPRESS AND COMPARE system offers an interactive visual interface to streamline analysis and improve understanding of model behavior changes.

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    Area of Science:

    • Computer Science
    • Machine Learning
    • Data Visualization

    Background:

    • Deploying machine learning models on-device requires compression algorithms to reduce model size and improve inference speed.
    • Effective comparison of compression experiments is crucial for analyzing accuracy-efficiency trade-offs and model behavior changes.
    • Existing tools often fragment the analysis process, leading to inefficiencies and incomplete insights.

    Purpose of the Study:

    • To develop an interactive visual system, COMPRESS AND COMPARE, that supports real-world comparative workflows for machine learning model compression.
    • To provide a unified interface for visualizing model provenance and comparing predictions, weights, and activations.

    Main Methods:

    • Development of the COMPRESS AND COMPARE interactive visual system.
    • Demonstration of the system's utility through case studies on generative language models and image classification models.
    • Evaluation of the system via a user study with eight machine learning compression experts.

    Main Results:

    • COMPRESS AND COMPARE effectively visualizes provenance relationships between compressed models.
    • The system reveals compression-induced behavior changes by comparing model predictions, weights, and activations.
    • User study indicated the system structures compression workflows, builds practitioner intuition, and encourages thorough analysis.

    Conclusions:

    • COMPRESS AND COMPARE enhances the analysis of machine learning model compression by providing a unified visual interface.
    • The system aids in debugging compression failures and identifying compression artifacts.
    • Identified compression-specific challenges and generalizable visualizations for future visual analytics tools in model comparison.