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cs.LG2026
Empirical Minimal-Realisation Compression of Deep Neural Networks via Controllability-Observability Tests
Anis Hamadouche, Amir Hussain
Deep neural networks often contain substantial hidden-state redundancy, but most compression methods operate directly on weights, neurons, or quantised representations without expl…
cs.LG2026
Lyapunov-Guided Training for Hardware-Safe Neural Networks Under Fixed-Point Arithmetic
Anis Hamadouche, Amir Hussain
Low-precision neural networks are attractive for resource-constrained hardware, but fixed-point arithmetic introduces failure modes that are often hidden by idealised quantisation…