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The softmax function is derived from the log-sum-exp function.
The log-sum-exp function is a specific case of the softmax function.
Both functions are identical in their outputs.
The log-sum-exp function cannot be used to compute probabilities.
Understanding the Answer
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Softmax turns raw scores into probabilities by dividing each exponential by the sum of all exponentials. Other options are incorrect because Some think log-sum-exp is a special case of softmax, but it is actually a helper that calculates the denominator; Softmax outputs a vector of probabilities, while log-sum-exp outputs a single number.
Key Concepts
Log-sum-exp Function
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Definition
The log-sum-exp function is a convex and differentiable approximation to the max function, commonly used in optimization and machine learning algorithms. It provides a smooth representation of the maximum value among a set of numbers.
Topic Definition
The log-sum-exp function is a convex and differentiable approximation to the max function, commonly used in optimization and machine learning algorithms. It provides a smooth representation of the maximum value among a set of numbers.
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