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The bias–variance tradeoff is a fundamental concept in machine learning and statistics describing the balance between the error introduced by the model’s assumptions and the error introduced by sensitivity to fluctuations in the training data. It explains how models with high bias oversimplify the data, while models with high variance overfit the data.
VICReg is a regularization technique used in machine learning to ensure the learned representations are both invariant to certain transformations and exhibit specific statistical properties.
Dropout is a regularization technique used in neural networks to reduce overfitting by randomly deactivating units during training. It improves model generalization by preventing complex co-adaptations between neurons.
Regularization in mathematics refers to techniques used to modify ill-posed problems or to impose smoothness or other constraints on solutions to achieve stability and uniqueness. It is commonly applied in inverse problems, optimization, and statistical modeling to prevent overfitting or to handle incomplete or noisy data.