CWRNN-INVR combines WarpRNN for structured video information and residual grids for irregular details to reach 33.73 dB average PSNR on the UVG dataset at 3M parameters, outperforming existing INVR methods.
Coupled oscillatory recurrent neural network (cornn): An accurate and (gradient) stable architecture for learning long time dependencies
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Upper generalization bounds for neural oscillators scale polynomially with MLP size and time length, avoiding the curse of parametric complexity, with numerical validation on a Bouc-Wen nonlinear system.
Upper bounds are derived showing that neural oscillator approximation errors for causal operators and stable second-order dynamical systems scale polynomially with the reciprocals of the widths of the two MLPs.
citing papers explorer
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CWRNN-INVR: A Coupled WarpRNN based Implicit Neural Video Representation
CWRNN-INVR combines WarpRNN for structured video information and residual grids for irregular details to reach 33.73 dB average PSNR on the UVG dataset at 3M parameters, outperforming existing INVR methods.
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Upper Generalization Bounds for Neural Oscillators
Upper generalization bounds for neural oscillators scale polynomially with MLP size and time length, avoiding the curse of parametric complexity, with numerical validation on a Bouc-Wen nonlinear system.
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Upper Approximation Bounds for Neural Oscillators
Upper bounds are derived showing that neural oscillator approximation errors for causal operators and stable second-order dynamical systems scale polynomially with the reciprocals of the widths of the two MLPs.