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cs.LG2026
LipSSM: Structurally Lipschitz-Bounded Cascaded State-Space Model via Metric Transfer between Consecutive SSM Layers
Natsuki Yoshino, Ren Uchida, Kazuki Matsumoto +1
Lipschitz continuity is a fundamental principle in the design of certifiably robust deep neural networks (DNNs), wherein adjusting the Lipschitz constant, which quantifies network…
cs.LG2026
PolarBM: Complex-valued Boltzmann Machine for Modeling Audio Signals in Polar and Log-polar Coordinates
Toru Nakashika, Kohei Yatabe
Although vast amounts of data, such as audio signal spectra, are naturally represented using complex numbers, conventional machine learning methods often simplify complex-domain pr…
cs.LG2026
Learn and Verify: A Framework for Rigorous Verification of Physics-Informed Neural Networks
Kazuaki Tanaka, Kohei Yatabe
The numerical solution of differential equations using neural networks has become a central topic in scientific computing, with Physics-Informed Neural Networks (PINNs) emerging as…