Difference between revisions of "Energy-based Model (EBN)"
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* [[Energy]] | * [[Energy]] | ||
| − | * [[Algorithms]] | + | * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]] |
* [[Immersive Reality]] ... [[Metaverse]] ... [[Digital Twin]] ... [[Internet of Things (IoT)]] ... [[Transhumanism]] | * [[Immersive Reality]] ... [[Metaverse]] ... [[Digital Twin]] ... [[Internet of Things (IoT)]] ... [[Transhumanism]] | ||
Revision as of 04:40, 14 July 2023
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- Energy
- AI Solver ... Algorithms ... Administration ... Model Search ... Discriminative vs. Generative ... Optimizer ... Train, Validate, and Test
- Immersive Reality ... Metaverse ... Digital Twin ... Internet of Things (IoT) ... Transhumanism
Energy-Based Models (EBMs) discover data dependencies by applying a measure of compatibility (scalar energy) to each configuration of the variables. For a model to make a prediction or decision (inference) it needs to set the value of observed variables to 1 and finding values of the remaining variables that minimize that “energy” level.
EBMs are also known as non-normalized probabilistic models, specify probability density or mass functions up to an unknown normalizing constant. Unlike most other probabilistic models, EBMs do not place a restriction on the tractability of the normalizing constant, thus are more flexible.
