-
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…
-
Sparse Attention: When Less Context Is More
In the early years of modern neural language models, the dominant strategy for improving artificial intelligence systems was simple: provide the model with more data, larger context windows, and increasingly complex architectures. Transformers, first introduced in 2017, quickly became the backbone of natural language processing systems because of their ability to evaluate relationships between every…