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MMLU / Statement 1| The derivative of the sigmoid σ(x)=(1+e^(−x))^(−1) with…

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question

Statement 1| The derivative of the sigmoid σ(x)=(1+e−x)−1\sigma(x)=(1+e^{-x})^{-1} with respect to xx is equal to Var(B)\text{Var}(B) where B∼Bern(σ(x))B\sim \text{Bern}(\sigma(x)) is a Bernoulli random variable. Statement 2| Setting the bias parameters in each layer of neural network to 0 changes the bias-variance trade-off such that the model's variance increases and the model's bias decreases
Plain-text mathematical notation (without MathML)
Statement 1| The derivative of the sigmoid σ(x)=(1+e^(−x))^(−1) with respect to x is equal to Var(B) where B∼Bern(σ(x)) is a Bernoulli random variable. Statement 2| Setting the bias parameters in each layer of neural network to 0 changes the bias-variance trade-off such that the model's variance increases and the model's bias decreases
Original LaTeX notation
Statement 1| The derivative of the sigmoid $\sigma(x)=(1+e^{-x})^{-1}$ with respect to $x$ is equal to $\text{Var}(B)$ where $B\sim \text{Bern}(\sigma(x))$ is a Bernoulli random variable. Statement 2| Setting the bias parameters in each layer of neural network to 0 changes the bias-variance trade-off such that the model's variance increases and the model's bias decreases

choices

  1. True, True
  2. False, False
  3. True, False
  4. False, True

subject

machine_learning

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Source and history

task_id
17ff1012-209d-5ddc-ac95-cf462d4ff7d9
task_key
all--test--17ff1012-209d-5ddc-ac95-cf462d4ff7d9
task_revision_id
1
config / split
all / test

Official source

initial import