Pruning-induced phases in fully-connected neural networks: the eumentia, the dementia, and the amentia
Modern neural networks are heavily overparameterized, and pruning, which removes redundant neurons or connections, has emerged as a key approach to compressing them without sacrificing performance. However, while practical pruning methods are well developed, whether pruning induces sharp phase transitions in the neural networks and, if so, to what universality class they belong, remain open questions. To address this, we study fully-connected neural networks trained on MNIST, independently varying the dropout (i.e., removing neurons) rate at both the training and evaluation stages to map the phase diagram. We identify three distinct phases: eumentia (the network learns), dementia (the net...
| Authors | Haining Pan, Nakul Aggarwal, J. H. Pixley |
|---|---|
| Primary category | cond-mat.dis-nn |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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