Architecture design for deep networks is fundamentally about balancing rank preservation (keeping information flowing), ensemble-like behavior (layers staying independent), and parameter efficiency—skip connections and normalization placement control this tradeoff by managing how much gradient ...
This paper reveals how Transformer architecture components preserve information flow across deep networks by maintaining mathematical rank. Skip connections and layer normalization prevent rank collapse—where information gets squeezed into fewer dimensions—by routing gradients around lossy operations.