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Pretrained neural networks can adapt their architecture dynamically for different inputs, improving efficiency and performance by customizing layer usage without finetuning, as shown through Monte Carlo Tree Search optimization.
https://arxiv.org/abs//2507.07996
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
By Igor Melnyk5
33 ratings
Pretrained neural networks can adapt their architecture dynamically for different inputs, improving efficiency and performance by customizing layer usage without finetuning, as shown through Monte Carlo Tree Search optimization.
https://arxiv.org/abs//2507.07996
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

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