Energy-based models in machine learning
GPTKB entity
Statements (91)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:model
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| gptkbp:hasConcept |
gptkb:organization
inference loss function maximum likelihood estimation partition function sampling parameter estimation adversarial learning contrastive learning denoising score matching energy function energy landscape energy minimization energy score energy-based anomaly detection energy-based applications energy-based benchmarking energy-based bio-inspired computing energy-based calibration energy-based classification energy-based cloud computing energy-based clustering energy-based continual learning energy-based distributed learning energy-based domain adaptation energy-based edge computing energy-based efficiency energy-based ensemble models energy-based explainability energy-based fairness energy-based federated learning energy-based few-shot learning energy-based generalization energy-based hardware acceleration energy-based hybrid models energy-based hyperparameter tuning energy-based inference energy-based interpretability energy-based loss energy-based meta-learning energy-based model evaluation energy-based model selection energy-based multi-task learning energy-based neuromorphic computing energy-based optimization energy-based out-of-distribution detection energy-based overfitting energy-based parallelization energy-based privacy energy-based quantum computing energy-based regression energy-based regularization energy-based regularization techniques energy-based reinforcement learning energy-based representation learning energy-based research energy-based robustness energy-based sampling energy-based scalability energy-based security energy-based semi-supervised learning energy-based transfer learning energy-based transferability energy-based uncertainty estimation energy-based underfitting energy-based zero-shot learning latent variables mode collapse negative log-likelihood noise-contrastive estimation normalization constant partition function estimation sampling-based learning score matching score-based generative models self-normalizing models training difficulty |
| https://www.w3.org/2000/01/rdf-schema#label |
Energy-based models in machine learning
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| gptkbp:relatedTo |
gptkb:Markov_random_field
gptkb:Deep_energy-based_models gptkb:Gibbs_sampling gptkb:Hopfield_network gptkb:Restricted_Boltzmann_machine Boltzmann machine Contrastive divergence |
| gptkbp:usedIn |
unsupervised learning
generative modeling structured prediction |
| gptkbp:bfsParent |
gptkb:Yann_LeCun
|
| gptkbp:bfsLayer |
4
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