Flat Channels to Infinity in Neural Loss Landscapes

The study on neural loss landscapes uncovers special channels where loss decreases slowly, leading to the divergence of output weights of neurons to infinity. At convergence, these neurons function as gated linear units, which highlights a surprising aspect of their computational capabilities. This research is crucial as it connects to existing optimization methods such as SGD and ADAM, which are likely to encounter these channels during training. By characterizing these quasi-flat regions, the study provides a comprehensive view of gradient dynamics and geometry, potentially guiding future advancements in AI model training and optimization.