ClaimsYann LeCun

“Because regularized methods are less likely than contrastive methods to fall victim to the curse of dimensionality, they seem more promising to train EBM of the future.”

Dawid and LeCun, lecture notes authors June 2023 Introduction to Latent Variable Energy-Based Models (Les Houches lecture notes) (arXiv) archived copy

Prediction AI timelines and forecasting

Regularized methods seem more promising than contrastive methods for training future energy-based models, because they are less likely to fall victim to the curse of dimensionality.

Unresolvable The public record could not settle it.

Prediction: Statements about events outside the person's control, checked once they fell due: did they come true?

Reasoning

Open-ended prediction about future promise of methods with no horizon.

Cite this claim

Yann LeCun, “Regularized methods seem more promising than contrastive methods for training future energy-based models, because they are less likely to fall victim to the curse of dimensionality.” (said June 2023). Verdict: unresolvable. Reliability API, claim c_70f2f61cf8fd, method 0.1.0+c876030c, dataset release v1.1.2. https://epistacklabs.com/claims/c_70f2f61cf8fd

This claim is in dataset release v1.1.2, with the same verdict.

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More prediction claims by Yann LeCun

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  4. Mostly true Prediction 2019
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  5. Mostly true Prediction 2010
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All of them

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