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Glossario IA

Il dizionario completo dell'Intelligenza Artificiale

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categorie
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sottocategorie
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Supernetwork

Over-parameterized neural network containing all candidate architectures as subgraphs, enabling simultaneous evaluation of multiple architectures through weight sharing between different configurations.

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Path Sampling

Random sampling method of subpaths in the supernetwork during training, ensuring all potential architectures receive equal attention and avoiding selection bias.

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Sub-network Sampling

Process of extracting and evaluating specific architectures from the trained supernetwork, sampling different configurations to identify the best-performing ones without additional training cost.

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Performance Predictor

Model trained to estimate the performance of an architecture without full evaluation, using structural features to efficiently guide the search toward promising architectures.

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Once-for-All Network

Advanced One-Shot NAS architecture enabling deployment on different hardware constraints without retraining, using a progressively shrunk supernetwork to adapt to available resources.

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Proxyless NAS

One-Shot NAS approach eliminating the use of proxy datasets or smaller models, training directly on the target dataset while maintaining computational efficiency.

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Single-path NAS

One-Shot NAS variant simplifying the supernetwork to a single path during training, reducing complexity and improving stability while preserving architectural exploration capability.

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FairNAS

One-Shot NAS approach ensuring fair treatment of all candidate architectures during training, eliminating performance biases and ensuring impartial evaluation of configurations.

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FBNet

Facebook Berkeley Network, One-Shot NAS framework that directly integrates hardware constraints into the search process, optimizing architectures for specific platforms in real time.

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Neural Architecture Parameters

Continuous parameters representing the weights of architectural decisions in differentiable approaches, optimized alternately with network weights to converge towards an optimal architecture.

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Architecture Gradient

Gradient of architecture parameters with respect to the objective function, allowing direct update of structural decisions in One-Shot NAS approaches based on differentiable optimization.

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