Papers › Neural network is heterogeneous: Phase matters more
Neural network is heterogeneous: Phase matters more
Yuqi Nie, Hui Yuan
We find a heterogeneity in both complex and real valued neural networks with the insight from wave optics, claiming a much more important role of phase than its amplitude counterpart in the weight matrix. In complex-valued neural networks, we show that among different types of pruning, the weight matrix with only phase information preserved achieves the best accuracy, which holds robustly under various settings of depth and width. The conclusion can be generalized to real-valued neural networks, where signs take the place of phases. These inspiring findings enrich the techniques of network pruning and binary computation.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections