A universal approximation theorem of deep neural networks for expressing probability distributions

Keyphrases

• Probability Distribution 100%
• Deep Neural Network 100%
• Universal Approximation Theorem 100%
• Neural Network 50%
• Mean Distance 50%
• Wasserstein Distance 50%
• Stein Discrepancy 50%
• Three-class 25%
• Approximation Error 25%
• Source Distribution 25%
• Empirical Measure 25%
• Target Distribution 25%
• Convergence Estimates 25%
• ReLU Activation 25%
• Push-forward Measure 25%
• Discrete Optimal Transport 25%
• Integral Probability Metrics 25%
• Universal Approximation Property 25%

Mathematics

• Probability Distribution 100%
• Approximation Theorem 100%
• Deep Neural Network 100%
• Neural Network 50%
• Wasserstein Distance 50%
• Probability Theory 25%
• Upper Bound 25%
• Approximation Error 25%
• Optimal Transport 25%
• Approximation Property 25%
• target distribution π 25%
• Closeness 25%
• Push-Forward 25%