Loss functions in Instance Representation Learning [R]

Short summary

Technical Q&A from r/MachineLearning questioning why Noise-Contrastive Estimation is used as an intermediate approximation step for intractable MLE objectives in instance representation learning when it still requires denominator computation. The poster sought clarification on biased estimators and gradient convergence behavior but remained unclear on the connection between NCE's density estimation formulation and its practical computational benefits.

• •NCE approximates expensive MLE losses with reduced computation but still estimates a denominator
• •User questions why NCE's denominator approximation doesn't directly replace the original objective
• •Technical confusion around biased estimator theory and how sample size affects gradient convergence

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