Introduces M-information as a scalable measure of higher-order information integration in multivariate time series, computed via convex optimization and tested on neuronal and neuroimaging data.
Deep learning,
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Deep learning receivers enable reliable FTN signaling with up to 75% spectral compression via sliding-window detection while maintaining low latency and robustness to channel variations.
Intelligence Inertia models the computational resistance to structural change in neural networks via a heuristic relativistic analogy, yielding a J-shaped cost curve that diverges from classical approximations.
citing papers explorer
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A scalable estimator of higher-order information in complex dynamical systems
Introduces M-information as a scalable measure of higher-order information integration in multivariate time series, computed via convex optimization and tested on neuronal and neuroimaging data.
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Neural Equalisers for Highly Compressed Faster-than-Nyquist Signalling: Design, Performance, Complexity and Robustness
Deep learning receivers enable reliable FTN signaling with up to 75% spectral compression via sliding-window detection while maintaining low latency and robustness to channel variations.
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Intelligence Inertia: Physical Isomorphism and Applications
Intelligence Inertia models the computational resistance to structural change in neural networks via a heuristic relativistic analogy, yielding a J-shaped cost curve that diverges from classical approximations.