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Tag: network

  • Teaching a Neural Network the Mandelbrot Set

    Teaching a Neural Network the Mandelbrot Set And why Fourier features change everything The post Teaching a Neural Network the Mandelbrot Set appeared first on Towards Data Science. Carlos Redondo Go to original source

  • The Machine Learning “Advent Calendar” Day 17: Neural Network Regressor in Excel

    The Machine Learning “Advent Calendar” Day 17: Neural Network Regressor in Excel Neural networks often feel like black boxes. In this article, we build a neural network regressor from scratch using only Excel formulas. By making every step explicit, from forward propagation to backpropagation, we show how a neural network learns to approximate non-linear functions…

  • A Fully Probabilistic Tensor Network for Regularized Volterra System Identification

    A Fully Probabilistic Tensor Network for Regularized Volterra System Identification arXiv:2511.20457v1 Announce Type: new Abstract: Modeling nonlinear systems with Volterra series is challenging because the number of kernel coefficients grows exponentially with the model order. This work introduces Bayesian Tensor Network Volterra kernel machines (BTN-V), extending the Bayesian Tensor Network framework to Volterra system identification.…

  • I Measured Neural Network Training Every 5 Steps for 10,000 Iterations

    I Measured Neural Network Training Every 5 Steps for 10,000 Iterations Image by Pixabay.com The post I Measured Neural Network Training Every 5 Steps for 10,000 Iterations appeared first on Towards Data Science. Javier Marin Go to original source

  • PrAda-GAN: A Private Adaptive Generative Adversarial Network with Bayes Network Structure

    PrAda-GAN: A Private Adaptive Generative Adversarial Network with Bayes Network Structure arXiv:2511.07997v1 Announce Type: new Abstract: We revisit the problem of generating synthetic data under differential privacy. To address the core limitations of marginal-based methods, we propose the Private Adaptive Generative Adversarial Network with Bayes Network Structure (PrAda-GAN), which integrates the strengths of both GAN-based…

  • Interpretable Network-assisted Random Forest+

    Interpretable Network-assisted Random Forest+ arXiv:2509.15611v1 Announce Type: new Abstract: Machine learning algorithms often assume that training samples are independent. When data points are connected by a network, the induced dependency between samples is both a challenge, reducing effective sample size, and an opportunity to improve prediction by leveraging information from network neighbors. Multiple methods taking…

  • Contrastive Network Representation Learning

    Contrastive Network Representation Learning arXiv:2509.11316v1 Announce Type: new Abstract: Network representation learning seeks to embed networks into a low-dimensional space while preserving the structural and semantic properties, thereby facilitating downstream tasks such as classification, trait prediction, edge identification, and community detection. Motivated by challenges in brain connectivity data analysis that is characterized by subject-specific, high-dimensional,…

  • Testing for correlation between network structure and high-dimensional node covariates

    Testing for correlation between network structure and high-dimensional node covariates arXiv:2509.03772v1 Announce Type: new Abstract: In many application domains, networks are observed with node-level features. In such settings, a common problem is to assess whether or not nodal covariates are correlated with the network structure itself. Here, we present four novel methods for addressing this…

  • BaMANI: Bayesian Multi-Algorithm causal Network Inference

    BaMANI: Bayesian Multi-Algorithm causal Network Inference arXiv:2508.11741v1 Announce Type: new Abstract: Improved computational power has enabled different disciplines to predict causal relationships among modeled variables using Bayesian network inference. While many alternative algorithms have been proposed to improve the efficiency and reliability of network prediction, the predicted causal networks reflect the generative process but also…

  • Perfect Clustering in Very Sparse Diverse Multiplex Networks

    Perfect Clustering in Very Sparse Diverse Multiplex Networks arXiv:2507.19423v1 Announce Type: new Abstract: The paper studies the DIverse MultiPLEx Signed Generalized Random Dot Product Graph (DIMPLE-SGRDPG) network model (Pensky (2024)), where all layers of the network have the same collection of nodes. In addition, all layers can be partitioned into groups such that the layers…

  • From Reactive to Predictive: Forecasting Network Congestion with Machine Learning and INT

    From Reactive to Predictive: Forecasting Network Congestion with Machine Learning and INT Learn how machine learning can predict network congestion before it happens The post From Reactive to Predictive: Forecasting Network Congestion with Machine Learning and INT appeared first on Towards Data Science. Shireesh Kumar Singh Go to original source

  • GRAND: Graph Release with Assured Node Differential Privacy

    GRAND: Graph Release with Assured Node Differential Privacy arXiv:2507.00402v1 Announce Type: new Abstract: Differential privacy is a well-established framework for safeguarding sensitive information in data. While extensively applied across various domains, its application to network data — particularly at the node level — remains underexplored. Existing methods for node-level privacy either focus exclusively on query-based…

  • Why Are Convolutional Neural Networks Great For Images?

    Why Are Convolutional Neural Networks Great For Images? The Universal Approximation Theorem states that a neural network with a single hidden layer and a nonlinear activation function can approximate any continuous function.  Practical issues aside, such that the number of neurons in this hidden layer would grow enormously large, we do not need other network architectures. A simple…

  • Uncertainty quantification and posterior sampling for network reconstruction

    Uncertainty quantification and posterior sampling for network reconstruction arXiv:2503.07736v1 Announce Type: new Abstract: Network reconstruction is the task of inferring the unseen interactions between elements of a system, based only on their behavior or dynamics. This inverse problem is in general ill-posed, and admits many solutions for the same observation. Nevertheless, the vast majority of…

  • How to Create Network Graph Visualizations in Microsoft PowerBI

    How to Create Network Graph Visualizations in Microsoft PowerBI Microsoft PowerBI is a one of the most popular Business Intelligence (BI) tools, and while it has all the features you need to create dynamic analytic reporting for stakeholders across the business, creating some advanced data visualizations is more challenging. This article will walk through how…

  • Visualizing Neural Network Internals

    Visualizing Neural Network Internals sentdex Go to original source