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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.

Jimmy Lei Ba, also known as Jimmy Ba, is a computer scientist whose published work includes Adam and layer normalization. His personal research biography identifies Geoffrey Hinton as his doctoral supervisor and describes a focus on efficient algorithms for deep learning.^[ref] The 2016 layer-normalization manuscript prints his University of Toronto affiliation and the name Jimmy Lei Ba.^[ref]

Ba coauthored Adam: A Method for Stochastic Optimization by Diederik P. Kingma and Jimmy Lei Ba with Diederik P. Kingma. The algorithm maintains optimizer state based on gradients, so its update depends on the history of the optimization. Adam bias correction accounts for the early effect of initializing its exponential accumulators at zero.^[ref]

The layer normalization paper, coauthored with Jamie Ryan Kiros and Geoffrey E. Hinton, addresses a different part of a neural network: it computes a mean and variance across feature coordinates of an individual example, then applies learned gain and bias. It avoids making those statistics depend on the other examples in the mini-batch.^[ref]

An original numerical example illustrates one precise symmetry. In the stabilized formula of layer normalization, set all gains to 1, all biases to 0, and layer-normalization epsilon to $\epsilon =1/3$. For $z=(1,2,3)$, the coordinate arithmetic mean is 2 and the coordinate variance is $2/3$, so the normalized output is $(-1,0,1)$. Adding 10 to every coordinate gives $(11,12,13)$, whose output is again $(-1,0,1)$: layer normalization is invariant under a common input shift because the coordinate mean shifts by the same amount and the deviations remain unchanged.

The fixed layer-normalization epsilon affects scaling differently. With $2z=(2,4,6)$, the coordinate variance is $8/3$ and the output is $(-2/\sqrt{3},0,2/\sqrt{3})$, which differs from the original output. This calculation distinguishes the exact common-shift symmetry from idealized scale invariance when the stabilizer is omitted. It does not assign either numerical example to the paper's experiments.