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Tag: structured

  • Optimizing Vector Search: Why You Should Flatten Structured Data 

    Optimizing Vector Search: Why You Should Flatten Structured Data  An analysis of how flattening structured data can boost precision and recall by up to 20% The post Optimizing Vector Search: Why You Should Flatten Structured Data  appeared first on Towards Data Science. Oleg Tereshin Go to original source

  • GliNER2: Extracting Structured Information from Text

    GliNER2: Extracting Structured Information from Text From unstructured text to structured Knowledge Graphs The post GliNER2: Extracting Structured Information from Text appeared first on Towards Data Science. Tomaz Bratanic Go to original source

  • Theory and computation for structured variational inference

    Theory and computation for structured variational inference arXiv:2511.09897v1 Announce Type: new Abstract: Structured variational inference constitutes a core methodology in modern statistical applications. Unlike mean-field variational inference, the approximate posterior is assumed to have interdependent structure. We consider the natural setting of star-structured variational inference, where a root variable impacts all the other ones. We…

  • Plotly Dash — A Structured Framework for a Multi-Page Dashboard

    Plotly Dash — A Structured Framework for a Multi-Page Dashboard An easy starting point for larger and more complicated Dash dashboards The post Plotly Dash — A Structured Framework for a Multi-Page Dashboard appeared first on Towards Data Science. Michael Clayton Go to original source

  • Sample completion, structured correlation, and Netflix problems

    Sample completion, structured correlation, and Netflix problems arXiv:2509.20404v1 Announce Type: new Abstract: We develop a new high-dimensional statistical learning model which can take advantage of structured correlation in data even in the presence of randomness. We completely characterize learnability in this model in terms of VCN${}_{k,k}$-dimension (essentially $k$-dependence from Shelah’s classification theory). This model suggests…

  • Extracting Structured Data with LangExtract: A Deep Dive into LLM-Orchestrated Workflows

    Extracting Structured Data with LangExtract: A Deep Dive into LLM-Orchestrated Workflows A guide to building modular workflows for structured intelligence The post Extracting Structured Data with LangExtract: A Deep Dive into LLM-Orchestrated Workflows appeared first on Towards Data Science. Subha Ganapathi Go to original source

  • Using Google’s LangExtract and Gemma for Structured Data Extraction

    Using Google’s LangExtract and Gemma for Structured Data Extraction Extracting structured information effectively and accurately from long unstructured text with LangExtract and LLMs The post Using Google’s LangExtract and Gemma for Structured Data Extraction appeared first on Towards Data Science. Kenneth Leung Go to original source

  • Generating Structured Outputs from LLMs

    Generating Structured Outputs from LLMs An overview of popular techniques to confine LLMs’ output to a predefined schema The post Generating Structured Outputs from LLMs appeared first on Towards Data Science. Ibrahim Habib Go to original source

  • Sparse Gaussian Processes: Structured Approximations and Power-EP Revisited

    Sparse Gaussian Processes: Structured Approximations and Power-EP Revisited arXiv:2507.02377v1 Announce Type: new Abstract: Inducing-point-based sparse variational Gaussian processes have become the standard workhorse for scaling up GP models. Recent advances show that these methods can be improved by introducing a diagonal scaling matrix to the conditional posterior density given the inducing points. This paper first…

  • Structured LLM Output Using Ollama

    Structured LLM Output Using Ollama Control your model responses effectively Continue reading on Towards Data Science » Thomas Reid Go to original source

  • How to Use Structured Generation for LLM-as-a-Judge Evaluations

    How to Use Structured Generation for LLM-as-a-Judge Evaluations Structured generation is fundamental to building complex, multi-step reasoning agents in LLM evaluations — especially for open source models Source: Generated with SDXL 1.0 Disclosure: I am a maintainer of Opik, one of the open source projects used later in this article. For the past few months, I’ve been working on LLM-based…