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Tag: large

  • Large Data Limits of Laplace Learning for Gaussian Measure Data in Infinite Dimensions

    Large Data Limits of Laplace Learning for Gaussian Measure Data in Infinite Dimensions arXiv:2601.14515v1 Announce Type: new Abstract: Laplace learning is a semi-supervised method, a solution for finding missing labels from a partially labeled dataset utilizing the geometry given by the unlabeled data points. The method minimizes a Dirichlet energy defined on a (discrete) graph…

  • JSON Parsing for Large Payloads: Balancing Speed, Memory, and Scalability

    JSON Parsing for Large Payloads: Balancing Speed, Memory, and Scalability Benchmarking JSON libraries for large payloads The post JSON Parsing for Large Payloads: Balancing Speed, Memory, and Scalability appeared first on Towards Data Science. Subha Ganapathi Go to original source

  • Your Next ‘Large’ Language Model Might Not Be Large After All

    Your Next ‘Large’ Language Model Might Not Be Large After All A 27M-parameter model just outperformed giants like DeepSeek R1, o3-mini, and Claude 3.7 on reasoning tasks The post Your Next ‘Large’ Language Model Might Not Be Large After All appeared first on Towards Data Science. Moulik Gupta Go to original source

  • How to Perform Comprehensive Large Scale LLM Validation

    How to Perform Comprehensive Large Scale LLM Validation Learn how to validate large scale LLM applications The post How to Perform Comprehensive Large Scale LLM Validation appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Uncertainty Quantification for Large-Scale Deep Networks via Post-StoNet Modeling

    Uncertainty Quantification for Large-Scale Deep Networks via Post-StoNet Modeling arXiv:2508.01217v1 Announce Type: new Abstract: Deep learning has revolutionized modern data science. However, how to accurately quantify the uncertainty of predictions from large-scale deep neural networks (DNNs) remains an unresolved issue. To address this issue, we introduce a novel post-processing approach. This approach feeds the output…

  • Large Stepsizes Accelerate Gradient Descent for Regularized Logistic Regression

    Large Stepsizes Accelerate Gradient Descent for Regularized Logistic Regression arXiv:2506.02336v1 Announce Type: new Abstract: We study gradient descent (GD) with a constant stepsize for $ell_2$-regularized logistic regression with linearly separable data. Classical theory suggests small stepsizes to ensure monotonic reduction of the optimization objective, achieving exponential convergence in $widetilde{mathcal{O}}(kappa)$ steps with $kappa$ being the condition…

  • An Overview of Large Language Models for Statisticians

    An Overview of Large Language Models for Statisticians arXiv:2502.17814v1 Announce Type: new Abstract: Large Language Models (LLMs) have emerged as transformative tools in artificial intelligence (AI), exhibiting remarkable capabilities across diverse tasks such as text generation, reasoning, and decision-making. While their success has primarily been driven by advances in computational power and deep learning architectures,…

  • Large Language Models: A Short Introduction

    Large Language Models: A Short Introduction And why you should care about LLMs Image by author. There’s an acronym you’ve probably heard non-stop for the past few years: LLM, which stands for Large Language Model. In this article we’re going to take a brief look at what LLMs are, why they’re an extremely exciting piece of technology, why…

  • Efficient Large Dimensional Self-Organising Maps with PyTorch

    Efficient Large Dimensional Self-Organising Maps with PyTorch Because it’s fun to self-organise Continue reading on Towards Data Science » Mathieu d’Aquin Go to original source

  • Combining Large and Small LLMs to Boost Inference Time and Quality

    Combining Large and Small LLMs to Boost Inference Time and Quality Implementing Speculative and Contrastive Decoding Large Language models are comprised of billions of parameters (weights). For each word it generates, the model has to perform computationally expensive calculations across all of these parameters. Large Language models accept a sentence, or sequence of tokens, and…