arXiv:2602.06245v2 Announce Type: replace-cross Abstract: Neural-network techniques are often transferred across architecture families by analogy, but such transfer is valid only when the assumptions required by a technique are preserved. We introduce this idea as inheritance between model classes. Using a unified node-level framework with tensor-valued activations, we prove that generalized feedforward networks (GFFNs) form a strict subset of generalized convolutional networks (GCNNs), so GCNN properties transfer directly to GFFNs. The reverse direction is not automatic: standard CNN nodes us
This research, published in 2026, represents ongoing foundational work in AI theory, driven by the need for more robust and transferable neural network techniques.
A deeper theoretical understanding of neural network relationships can lead to more efficient model development, better performance, and more reliable AI systems.
This theoretical proof establishes a clear inheritance hierarchy between specific neural network architectures, which could inform future model design principles and optimization strategies.
- · AI researchers
- · AI model developers
- · Deep learning practitioners
- · Inefficient model development paradigms
Improved understanding of architectural relationships will streamline neural network design and training.
More robust and generalizable AI models could emerge from a principles-based approach to architectural inheritance.
This foundational work could reduce computational costs by enabling more effective transfer learning and model optimization across different tasks and datasets.
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Read at arXiv cs.LG