Which of the following is TRUE about softmax (mark all that apply).

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Question restatement: Which of the following is TRUE about softmax (mark all that apply).

Option by option analysis:

Option 1: "is not differentiable". This is incorrect. The softmax function is differentiable with respect to its inputs, which is essential for gradient-based learning. The Jacobian is well-defined and used during backpropagation.

Option 2: "provides a distribution over indices of inputs". This statement is correct. Softmax converts a vector of real numbers into a probability distribution over the input indices, with all probabilities nonnegative and summing to 1. This is the core purpose of softmax in many neural network applications like classification and attention weights.

Option 3: "is permutation invariant". This is incorrect. Softmax is not invariant to permutation of inputs. If you permute the input vector, the resulting probabilities simply permute in the same way; the function does not produce the same output for all permutations.

Option 4: "is a unique layer to transformers." This is incorrect. Softmax is a common activation used across many architectures, not unique to transformers. While transformers use softmax in attention, softmax itself is not exclusive to them and is used in various models.

Summary of reasoning: Only Option 2 correctly describes softmax as producing a distribution over input indices, and the other statements conflict with the differentiability, permutation behavior, and architectural ubiquity of softmax.

CS-7643-O01, OAN, OSZ Quiz #4: Module 3

Which of the following is TRUE about softmax (mark all that apply).

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