相关实验视频

Updated: Jan 6, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.2K

一种基于可解释线性模型的解释方法,具有四个关键特性

Yuecan Yuan, Zhan Ao Huang, Peng Li

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |September 24, 2025
    PubMed
    概括

    本研究引入了一种新方法,用于在视觉任务中解释深度神经网络 (DNN). 它通过创建更强大的和语义上有意义的突出性地图来增强可解释性,以便更好地进行特征归属分析.

    更多相关视频

    Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach04:35

    Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

    Published on: July 3, 2020

    3.7K
    Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills06:52

    Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

    Published on: September 17, 2019

    6.7K

    相关概念视频

    Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

    Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

    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.
    In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
    253
    Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

    Mechanistic Models: Compartment Models in Individual and Population Analysis

    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...
    221
    Linear time-invariant Systems01:23

    Linear time-invariant Systems

    A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
    The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
    832
    Linear Approximation in Time Domain01:21

    Linear Approximation in Time Domain

    Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
    For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
    310
    Classification of Systems-I01:26

    Classification of Systems-I

    Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
    Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
    528
    Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

    Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

    Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
    When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
    In the case of subcutaneously administered drugs,...
    267