Results 101 to 110 of about 2,032,124 (281)
On the Kolmogorov Complexity of Binary Classifiers
CoRR, 2022 We provide tight upper and lower bounds on the expected minimum Kolmogorov complexity of binary classifiers that are consistent with labeled samples. The expected size is not more than complexity of the target concept plus the conditional entropy of the labels given the sample.
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Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
Advanced Intelligent Discovery, EarlyView.This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
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Nihon Kikai Gakkai ronbunshu, 2014 The complexity or randomness was examined with the aid of Kolmogorov complexity for the flow about a turbulence wedge developed from a single roughness element on a flat plate.
Masashi ICHIMIYA +2 more
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The Kolmogorov Expression Complexity of Logics
Information and Computation, 1997 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Advanced Intelligent Systems, EarlyView.Physics‐Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains constrained by the limited uncertainty quantification (UQ) capabilities.
Ibai Ramirez +4 more
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The Intransitive Logic of Directed Cycles and Flipons Enhances the Evolution of Molecular Computers by Augmenting the Kolmogorov Complexity of Genomes. [PDF]
Int J Mol Sci, 2023 Herbert A.
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Advanced Intelligent Systems, EarlyView.Single‐cell Spatial Transcriptomics Analysis and Denoising Engine is introduced as a unified deep learning framework that jointly performs denoising, clustering, and gene prioritization in spatial transcriptomics. By integrating linear and nonlinear representations within a dual‐channel architecture, it improves robustness and accuracy, uncovers ...
Yaxuan Cui +11 more
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Normalized Unconditional ϵ-Security of Private-Key Encryption
Entropy, 2017 In this paper we introduce two normalized versions of non-perfect security for private-key encryption: one version in the framework of Shannon entropy, another version in the framework of Kolmogorov complexity.
Lvqing Bi, Songsong Dai, Bo Hu
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Exploiting Ferroelectric and Spintronic Dynamics for Neural Network Computation
Advanced Intelligent Systems, EarlyView.Ferroelectric and spintronic devices, relying on the control of polarization and magnetization, offer intrinsically fast, durable, energy‐efficient, and low‐latency building blocks for analog in‐memory computing. The hysteretic dynamics of an order parameter are leveraged to provide nonvolatile, multistate memory and nonlinear switching. Brain‐inspired
Dashiell Harrison +4 more
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The Kolmogorov complexity of random reals
Annals of Pure and Applied Logic, 2004 Crudely speaking, a real number is random iff it does not belong to any constructive set of measure 0. Randomness can be reformulated in terms of a (prefix-free) Kolmogorov complexity \(K(\alpha_{| n})\) of the initial segments of the real number \(\alpha\) (described as an infinite sequence of 0s and 1s): \(\alpha\) is random iff there exists a ...
Liang Yu 0004 +2 more
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