Jamie Ryan Kiros

Jamie Ryan Kiros coauthored the 2016 layer normalization paper with Jimmy Lei Ba and Geoffrey E. Hinton. The original paper gives the full byline Jamie Ryan Kiros and a University of Toronto affiliation. Its normalization statistics are computed across the inputs within one case, rather than across different cases in a batch.^[ref]

A small original implementation experiment tests that distinction without measuring training speed. Fix one case's two inputs at $h=(2,6)$, unit gains, zero biases and a variance stabilizer of $1$. The within-case mean is $4$ and variance is $4$, so its normalized outputs areEvaluate this case first beside another case $(-100,100)$ and then beside $(1000,2000)$. The stated isolated normalization computation returns the same output for $h$ in both evaluations, because neither companion enters its statistics. This tests a concrete dependency of the computation, not a claim about every layer of a complete network.

The paper also studies recurrent neural networks, computing the statistics separately at each time step.^[ref] That application makes the choice of averaging axes consequential. The paper establishes a collaborative contribution; its byline does not assign a separate invention to each author.