Uncertainty Principle in Neural Networks #sciencefather #researchscientist #neuralnetworks #network
In neural networks, the Uncertainty Principle manifests in the ...
In neural networks, the Uncertainty Principle manifests in the trade-off between model complexity and generalization. As models become more expressive, they capture finer details of data but risk overfitting, leading ...to poor performance on unseen inputs. Conversely, simpler models generalize better but may lack the capacity to learn intricate patterns. This balance mirrors Heisenberg’s Uncertainty Principle in physics, where precision in one domain (e.g., position) limits certainty in another (e.g., momentum). In deep learning, uncertainty quantification methods, such as Bayesian inference and dropout techniques, help measure model confidence, improving robustness in real-world applications.
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