SPIN Unprocessed July 8, 2026 ai_technology research
Empirical Minimal-Realisation Compression of Deep Neural Networks via Controllability-Observability Tests
View original on arxiv.orgOverview
arXiv:2607.05457v1 Announce Type: new Abstract: Deep neural networks often contain substantial hidden-state redundancy, but most compression methods operate directly on weights, neurons, or quantised representations without explicitly characterising the dynamical role of internal states. This paper proposes a controllability-observability framework for empirical state-order reduction of deep neural networks. By viewing a trained network as a depth-indexed nonlinear dynamical system, we construct
SpinGraph analysis pending — check back after processing.
Ask AI about this story
Opens with the SpinGraph .md URL and structured context — one click, prompt included.
More from arXiv Machine Learning
View all →- Safe Bayesian Optimization with Counterfactual Policies
- A Coin Flip Per Token: Bernoulli Sparse Steering of Large Language Models
- SafeImpute: Reliable Clinical Data Imputation via Conformal Selection
- EquiFiLM: Charge-Conditioned Equivariant Force Fields via Feature-wise Linear Modulation
- Federated Physics-Grounded Reinforcement Learning for Distributed Stability Control in Smart Grids
- Self-Review Reinforcement Learning (SRRL) with Cross-Episode Memory and Policy Distillation
Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO