Beyond Market Intelligence/generative models

generative models

generative models on Beyond Market Intelligence: a running collection of 4 stories we have gathered and hand-picked because they are worth your time. Every post here touches on generative models in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around generative models, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.

Towards Data Science

Variational Autoencoders (VAEs) Explained: From Theory to ELBO and the Reparameterization Trick

Delve into Variational Autoencoders (VAEs), a powerful generative modeling technique, with our comprehensive, math-first walkthrough. This post systematically explores VAE theory, from the core concepts to the crucial Evidence Lower Bound (ELBO) and the reparameterization trick—essential for enabling efficient training. Understand how VAEs learn to generate new data by mastering these key components. For those seeking to build robust data infrastructure for AI agents, consider our related article, "Building an Agent-Ready Data Warehouse," which highlights common architectural pitfalls.

Machine Learning

"Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation", Gladstone et al. 2026 [R]

Gladstone et al.'s forthcoming paper, "Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation," introduces a significant advancement in AI model development. This work proposes a novel pretraining strategy, expanding beyond existing approaches to enable more intuitive and capable generative models. The research promises to reshape how we approach data-driven AI, offering a future-focused path toward more adaptable and efficient systems. For a broader perspective on the current landscape of machine learning research, explore our discussion on regaining coherence in the field.

KDnuggets

An Introductory Guide to Practical Constraint Decoding

Tired of wrestling with model outputs and chasing valid data formats? This introductory guide to practical constraint decoding equips you with a straightforward approach to ensuring predictable, structured results. You'll learn to move beyond generic prompts and directly guide your models toward desired outputs—no more begging for clean JSON! Discover a powerful technique to enhance data reliability and streamline your workflows. For deeper insights into related visualization techniques, explore "GPT-2 Small’s embedding geometry around “Trump”," available on our site.

Machine Learning

Anyone heading to Jeju for KDD? Let's meet up! 🙋[D]

Heading to KDD in Jeju? Let’s connect! We'd love to meet fellow attendees exploring the frontiers of AI. Specifically, we’re keen to engage with those focused on interpretability, fairness, and the editing of text-to-image models—though conversations on any topic are welcome. If you're interested in learning more about iterative RAG generation approaches, check out our recent article, "Loop Engineering for RAG Generation." We land on the 8th and invite you to reach out for coffee, discussion, or simply to share experiences.