neural architecture highway

**Highway Networks** are **deep feedforward networks that use gating mechanisms to regulate information flow across layers** — extending skip connections with learnable gates that control how much information passes through the transformation versus the skip path. **How Do Highway Networks Work?** - **Formula**: $y = T(x) cdot H(x) + C(x) cdot x$ where $T$ is the transform gate and $C$ is the carry gate. - **Simplification**: Typically $C = 1 - T$: $y = T(x) cdot H(x) + (1 - T(x)) cdot x$. - **Gate**: $T(x) = sigma(W_T x + b_T)$ (learned sigmoid gate). - **Paper**: Srivastava et al. (2015). **Why It Matters** - **Pre-ResNet**: One of the first architectures to successfully train 50-100+ layer networks. - **Learned Skip**: Unlike ResNet's fixed skip connections ($y = F(x) + x$), Highway Networks learn when to skip. - **LSTM Connection**: Highway Networks are essentially feedforward LSTMs — same gating principle. **Highway Networks** are **LSTM gates for feedforward networks** — the learned bypass mechanism that preceded and inspired ResNet's simpler identity shortcuts.