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Tag: embeddings

  • Likelihood-Preserving Embeddings for Statistical Inference

    Likelihood-Preserving Embeddings for Statistical Inference arXiv:2512.22638v1 Announce Type: new Abstract: Modern machine learning embeddings provide powerful compression of high-dimensional data, yet they typically destroy the geometric structure required for classical likelihood-based statistical inference. This paper develops a rigorous theory of likelihood-preserving embeddings: learned representations that can replace raw data in likelihood-based workflows — hypothesis testing,…

  • The Machine Learning “Advent Calendar” Day 22: Embeddings in Excel

    The Machine Learning “Advent Calendar” Day 22: Embeddings in Excel Understanding text embeddings through simple models and Excel The post The Machine Learning “Advent Calendar” Day 22: Embeddings in Excel appeared first on Towards Data Science. angela shi Go to original source

  • How Deep Feature Embeddings and Euclidean Similarity Power Automatic Plant Leaf Recognition

    How Deep Feature Embeddings and Euclidean Similarity Power Automatic Plant Leaf Recognition Introduction Automatic plant leaf detection is a remarkable innovation in computer vision and machine learning, enabling the identification of plant species by examining a photograph of the leaves. Deep learning is applied to extract meaningful features from an image of leaves and convert…

  • MMbeddings: Parameter-Efficient, Low-Overfitting Probabilistic Embeddings Inspired by Nonlinear Mixed Models

    MMbeddings: Parameter-Efficient, Low-Overfitting Probabilistic Embeddings Inspired by Nonlinear Mixed Models arXiv:2510.22198v1 Announce Type: new Abstract: We present MMbeddings, a probabilistic embedding approach that reinterprets categorical embeddings through the lens of nonlinear mixed models, effectively bridging classical statistical theory with modern deep learning. By treating embeddings as latent random effects within a variational autoencoder framework, our…

  • Positional Embeddings in Transformers: A Math Guide to RoPE & ALiBi

    Positional Embeddings in Transformers: A Math Guide to RoPE & ALiBi Learn APE, RoPE, and ALiBi positional embeddings for GPT — intuitions, math, PyTorch code, and experiments on TinyStories The post Positional Embeddings in Transformers: A Math Guide to RoPE & ALiBi appeared first on Towards Data Science. Sathya Krishnan Suresh Go to original source

  • Kernel Quantile Embeddings and Associated Probability Metrics

    Kernel Quantile Embeddings and Associated Probability Metrics arXiv:2505.20433v1 Announce Type: new Abstract: Embedding probability distributions into reproducing kernel Hilbert spaces (RKHS) has enabled powerful nonparametric methods such as the maximum mean discrepancy (MMD), a statistical distance with strong theoretical and computational properties. At its core, the MMD relies on kernel mean embeddings to represent distributions…

  • A Dictionary of Closed-Form Kernel Mean Embeddings

    A Dictionary of Closed-Form Kernel Mean Embeddings arXiv:2504.18830v1 Announce Type: new Abstract: Kernel mean embeddings — integrals of a kernel with respect to a probability distribution — are essential in Bayesian quadrature, but also widely used in other computational tools for numerical integration or for statistical inference based on the maximum mean discrepancy. These methods…

  • NLP Illustrated, Part 2: Word Embeddings

    NLP Illustrated, Part 2: Word Embeddings An illustrated and intuitive guide to word embeddings Continue reading on Towards Data Science » Shreya Rao Go to original source