# Rylan Schaeffer

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### Deep Linear Neural Networks

A deep linear neural network is a multi-layered neural network with the non-linear "activations" removed. That is, it is a composition of linear transformations (typically matrices). For instance, $\hat{y} = W_{32} W_{21} x$ is a two-layer linear network. Despite lacking the ability to learn non-linear functions, this class of network is attractive because it can be tractably analyzed and because its behaves similarly to its non-linear cousins under mild conditions.

One question we might immediately ask is for a two-layer linear neural network trained under mean squared error (MSE) on dataset $\{(x_i, y_i)\}_1^N$, what are the coupled differential equations that describe the network's learning dynamics? Let $x \in \mathbb{R}^i$ be the input, $y \in \mathbb{R}^o$ be the output, and $L = \frac{1}{N} \sum_{i=1}^N (y - W_{32} W_{21} x)^T (y - W_{32} W_{21} x)$ be the objective function. Define the input-input correlation matrix $\Sigma_{11} = \frac{1}{N}\sum_{i=1}^N x x^T$ and the input-output correlation matrix $\Sigma_{31} = \frac{1}{N}\sum_{i=1}^N y x^T$. We first derive $-\frac{\partial L}{\partial W_{32}}$:

\begin{align*} -\frac{\partial L}{\partial W_{32}} &= -\frac{1}{\partial W_{32}} \frac{1}{N} \sum_{i=1}^N (y - W_{32} W_{21} x)^T (y - W_{32} W_{21} x) \nonumber \\ &= -\frac{1}{\partial W_{32}} \frac{1}{N} \sum_{i=1}^N tr(y^T y - 2 y^T W_{32} W_{21} x + x^T W_{21}^T W_{32}^T W_{32} W_{21} x) \nonumber \\ &= \frac{1}{N} \sum_{i=1}^N 2 \frac{1}{\partial W_{32}} tr(y^T W_{32} W_{21} x) - \frac{1}{\partial W_{32}}tr(x^T W_{21}^T W_{32}^T W_{32} W_{21} x) \nonumber \\ &= \frac{1}{N} \sum_{i=1}^N 2 y x^T W_{21}^T - 2 W_{32} W_{21} x x^T W_{21}^T \nonumber \\ &= 2 \Sigma_{31} W_{21}^T - 2 W_{32} W_{21} \Sigma_{11} W_{21}^T \nonumber \\ -\frac{\partial L}{\partial W_{32}} &= 2 (\Sigma_{31} - W_{32} W_{21} \Sigma_{11}) W_{21}^T \end{align*}

We similarly derive $-\frac{\partial L}{\partial W_{21}}$: \begin{align*} -\frac{\partial L}{\partial W_{21}} = 2 \Sigma_{32}^T (\Sigma_{31} - W_{32} W_{21} \Sigma_{11}) \end{align*}

Together, these coupled nonlinear differential equations define the learning dynamics of a linear neural network. One key aspect is that the learning dynamics are non-linear, meaning that deep linear networks are more sophisticated than shallow (i.e. single-layer) networks. These non-linear learning dynamics arise from the choice of error function: MSE produces a loss function that is quartic with respect to the weights, resulting in a gradient that is cubic with respect to the weights.

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