AI RESEARCH
Learning Inference Concurrency in DynamicGate MLP Structural and Mathematical Justification
arXiv CS.LG
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ArXi:2604.13546v1 Announce Type: new Conventional neural networks strictly separate learning and inference because if parameters are updated during inference, outputs become unstable and even the inference function itself is not well defined [1, 2, 3]. This paper shows that DynamicGate MLP structurally permits learning inference concurrency [4, 5