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Why Residual Connections Stabilize Deep Networks
As neural networks became deeper in the early 2010s, researchers encountered a surprising obstacle. Intuitively, adding more layers should allow a model to learn more complex representations and achieve higher accuracy. However, experiments showed that beyond a certain depth, neural networks often became harder to train and sometimes even performed worse than shallower models. This…
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Mixture-of-Experts: How Routing Actually Works
As artificial intelligence systems grow larger and more capable, researchers face a fundamental challenge: how to increase model capacity without proportionally increasing computational cost. Traditional dense neural networks process every input through every parameter, meaning that doubling the model size roughly doubles the computation required for each inference step. This limitation has driven the search…