Derives generalized formulas for KAN inference complexity using RM, BOP, and NABS metrics across B-spline, GRBF, Chebyshev, and Fourier variants.
Available: https://openreview.net/forum?id=Ozo7qJ5vZi
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A hybrid CNN-KAN model with dual-domain and multi-scale frequency enhancement predicts CSI more accurately than RNN, LSTM, GRU, CNN, and Transformer baselines on QuaDRiGa simulations.
GAI-NeRF combines geometric algebra attention and an adaptive ray tracing module inside a NeRF model to deliver more accurate and generalizable wireless channel predictions across varied indoor environments.
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Hardware-Oriented Inference Complexity of Kolmogorov-Arnold Networks
Derives generalized formulas for KAN inference complexity using RM, BOP, and NABS metrics across B-spline, GRBF, Chebyshev, and Fourier variants.
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ChannelKAN: Multi-Scale Dual-Domain Channel Prediction via Hybrid CNN-KAN Architecture
A hybrid CNN-KAN model with dual-domain and multi-scale frequency enhancement predicts CSI more accurately than RNN, LSTM, GRU, CNN, and Transformer baselines on QuaDRiGa simulations.
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A Geometric Algebra-informed NeRF Framework for Generalizable Wireless Channel Prediction
GAI-NeRF combines geometric algebra attention and an adaptive ray tracing module inside a NeRF model to deliver more accurate and generalizable wireless channel predictions across varied indoor environments.