Author

T. Čadež

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#machine learning Preprint Oct 2026

Neural scaling laws and evolution of learnable activation functions of Kolmogorov-Arnold networks

Kolmogorov-Arnold Networks (KANs) represent a compelling alternative to traditional Multi-Layer Perceptron (MLP)-based neural networks. By employing activation functions as learnable elements, KANs offer superior interpretability, making them suited for scientific domains. In this work, we investigate the neural scalin...

T. Čadež, Sanghoon Lee, Kyoung-Min Kim · 0 citations

Preprint Jul 2026

Marked vertex search on disordered graphs with Rosenzweig-Porter phases

These results establish a direct and quantitative link between random matrix disorder on graphs and the performance of continuous-time quantum walk search, and suggest that disorder, rather than being merely an obstacle, can be exploited as a tunable parameter in quantum search protocols.

Sabyasachi Chakraborty, T. Čadež, Sonjoy Majumder et al. · 0 citations