Kolmogorov-Arnold Networks for Genomic Tasks DOI Creative Commons
Oleksandr Cherednichenko, Maria Poptsova

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Дек. 11, 2024

Abstract Kolmogorov-Arnold Networks (KANs) emerged as a promising alternative for multilayer perceptrons in dense fully connected networks. Multiple attempts have been made to integrate KANs into various deep learning architectures the domains of computer vision and natural language processing. Integrating models genomic tasks has not explored. Here, we tested linear (LKANs) convolutional (CKANs) replacement MLP baseline classification generation sequences. We used three benchmark datasets: Genomic Benchmarks, Genome Understanding Evaluation, Flipon Benchmark. demonstrated that LKANs outperformed both CK-ANs on almost all datasets. CKANs can achieve comparable results but struggle with scaling over large number parameters. Ablation analysis KAN layers correlates model performance. Overall, show improving performance relatively small Unleashing potential different SOTA currently genomics requires further research.

Язык: Английский

Kolmogorov-Arnold Networks for Genomic Tasks DOI Creative Commons
Oleksandr Cherednichenko, Maria Poptsova

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Дек. 11, 2024

Abstract Kolmogorov-Arnold Networks (KANs) emerged as a promising alternative for multilayer perceptrons in dense fully connected networks. Multiple attempts have been made to integrate KANs into various deep learning architectures the domains of computer vision and natural language processing. Integrating models genomic tasks has not explored. Here, we tested linear (LKANs) convolutional (CKANs) replacement MLP baseline classification generation sequences. We used three benchmark datasets: Genomic Benchmarks, Genome Understanding Evaluation, Flipon Benchmark. demonstrated that LKANs outperformed both CK-ANs on almost all datasets. CKANs can achieve comparable results but struggle with scaling over large number parameters. Ablation analysis KAN layers correlates model performance. Overall, show improving performance relatively small Unleashing potential different SOTA currently genomics requires further research.

Язык: Английский

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