翻訳待ち：Automatically Differentiable Nonlinear Tensor Networks (ADNTNs) for Exponential Compression of Deep Neural Networks

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。

[2606.00130] Automatically Differentiable Nonlinear Tensor Networks (ADNTNs) for Exponential Compression of Deep Neural Networks [Submitted on 28 May 2026] Title:Automatically Differentiable Nonlinear Tensor Networks (ADNTNs) for Exponential Compression of Deep Neural Networks View a PDF of the paper titled Automatically Differentiable Nonlinear Tensor Networks (ADNTNs) for Exponential Compression of Deep Neural Networks, by Andrzej Cichocki and Michal Wietczak View PDF HTML (experimental) Abstract:We study Automatically Differentiable Nonlinear Tensor Networks (ADNTNs), a family of structured weight generators whose compact core tensors are trained end-to-end by reverse-mode automatic differentiation (AD). The approach can be viewed as a natural extension of low-rank adaptation and tensor factorisation: instead of using one low-rank matrix update, an ADNTN builds a large weight tensor through a hierarchy of small cores, nonlinear activations, and optional lateral mixing tensors. The paper focuses on three architectures: Tree Tensor Networks (TTNs), augmented TTNs (aTTNs) with boundary disentanglers, and Multi-scale Entanglement Renormalisation Ansatze (MERA). The formulation supports nonlinear activations, task-aware objectives, batching, and hardware-aware execution schedules. At the same time, the paper keeps a clear distinction between \emph{differentiating} a contraction program and making contraction free: AD does not remove the cost of large intermediates, poor contraction orders, or exact contraction of general loopy tensor networks. Extensive simulations on AlexNet and VGG-16 layers show per-layer compression ratios from roughly $2000\times$ to $77000\times$ in the studied settings, with accuracy often matching the dense baseline and, in several VGG-16 cases, improving it. These results are encouraging rather than final: they suggest that ADNTNs are a promising, mathematically structured, and hardware-aware route toward much smaller neural networks, provided that optimisation, contraction schedules, and deployment kernels are designed together. Comments: 6 figure, 28 pages, to be submitted to Journal and confrence Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2606.00130 [cs.LG] (or arXiv:2606.00130v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2606.00130 arXiv-issued DOI via DataCite (pending registration) Submission history From: Andrzej Cichocki [view email] [v1] Thu, 28 May 2026 19:43:10 UTC (4,058 KB) Full-text links: Access Paper: View a PDF of the paper titled Automatically Differentiable Nonlinear Tensor Networks (ADNTNs) for Exponential Compression of Deep Neural Networks, by Andrzej Cichocki and Michal Wietczak View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG new | recent | 2026-06 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)