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Related Concept Videos

Transformation01:26

Transformation

30
Microbial communities are dynamic environments where cell lysis releases free DNA into the surroundings. Other cells can take up this extracellular DNA through a process known as transformation.When a cell incorporates this foreign DNA into its genome, resulting in genetic modification, the process is known as transformation. Cells capable of this process are termed competent. Competence can be natural, as observed in certain bacteria and archaea, or artificially induced in the...
30
Observational Learning01:12

Observational Learning

210
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
210
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Steps in the Modeling Process01:14

Steps in the Modeling Process

241
Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
241
Introduction to Learning01:18

Introduction to Learning

472
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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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

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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,...
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ProactiV: Studying Deep Learning Model Behavior Under Input Transformations.

Vidya Prasad, Ruud J G van Sloun, Anna Vilanova

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    Summary
    This summary is machine-generated.

    This study introduces ProactiV, a visual analytics method for deep learning (DL) models. ProactiV helps developers proactively identify model breaking points under input transformations, improving real-world deployment.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Deep learning (DL) models offer significant performance gains but suffer from low interpretability, leading to unpredictable behavior in real-world applications.
    • Model failures often stem from discrepancies between training data domains and deployment data domains, necessitating methods to identify breaking points under input transformations.
    • Existing visual analytics (VA) methods primarily focus on per-class or instance-level analysis, limiting the study of global model behavior under varying inputs.

    Purpose of the Study:

    • To develop a model-agnostic visual analytics (VA) method, ProactiV, for proactively studying deep learning (DL) model behavior under input transformations.
    • To generalize the analysis beyond classification tasks, providing a global view of model behavior under co-occurring input transformations.
    • To enable identification and verification of model breaking points and gain insights into input characteristics and model biases.

    Main Methods:

    • ProactiV employs a novel input optimization technique to determine input modifications that yield desired outputs.
    • This optimization process generates data for large-scale analysis of both global and local model behavior under input transformations.
    • The method is model-agnostic, applicable to various DL architectures and tasks, including classification and image-to-image translation.

    Main Results:

    • ProactiV facilitates proactive identification and verification of deep learning model breaking points.
    • The input optimization method reveals input characteristics crucial for desired outputs and helps uncover model biases.
    • Demonstrated effectiveness across diverse tasks, including image classification and image-to-image translation.

    Conclusions:

    • ProactiV offers a scalable and comprehensive approach to understanding deep learning model behavior under input transformations.
    • The method enhances model interpretability and robustness by enabling proactive identification of failure modes.
    • ProactiV supports developers in building more reliable and trustworthy deep learning systems.