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Tag: generative

  • Uncovering Physical Drivers of Dark Matter Halo Structures with Auxiliary-Variable-Guided Generative Models

    Uncovering Physical Drivers of Dark Matter Halo Structures with Auxiliary-Variable-Guided Generative Models arXiv:2602.23518v1 Announce Type: new Abstract: Deep generative models (DGMs) compress high-dimensional data but often entangle distinct physical factors in their latent spaces. We present an auxiliary-variable-guided framework for disentangling representations of thermal Sunyaev-Zel’dovich (tSZ) maps of dark matter halos. We introduce halo mass…

  • Generative AI, Discriminative Human

    Generative AI, Discriminative Human How to think critically about AI in an ocean of hype The post Generative AI, Discriminative Human appeared first on Towards Data Science. Jason Tamara Widjaja Go to original source

  • Error Analysis of Bayesian Inverse Problems with Generative Priors

    Error Analysis of Bayesian Inverse Problems with Generative Priors arXiv:2601.17374v1 Announce Type: new Abstract: Data-driven methods for the solution of inverse problems have become widely popular in recent years thanks to the rise of machine learning techniques. A popular approach concerns the training of a generative model on additional data to learn a bespoke prior…

  • Generative Conditional Missing Imputation Networks

    Generative Conditional Missing Imputation Networks arXiv:2601.00517v1 Announce Type: new Abstract: In this study, we introduce a sophisticated generative conditional strategy designed to impute missing values within datasets, an area of considerable importance in statistical analysis. Specifically, we initially elucidate the theoretical underpinnings of the Generative Conditional Missing Imputation Networks (GCMI), demonstrating its robust properties in…

  • On Conditional Stochastic Interpolation for Generative Nonlinear Sufficient Dimension Reduction

    On Conditional Stochastic Interpolation for Generative Nonlinear Sufficient Dimension Reduction arXiv:2512.18971v1 Announce Type: new Abstract: Identifying low-dimensional sufficient structures in nonlinear sufficient dimension reduction (SDR) has long been a fundamental yet challenging problem. Most existing methods lack theoretical guarantees of exhaustiveness in identifying lower dimensional structures, either at the population level or at the sample…

  • Generative modeling of conditional probability distributions on the level-sets of collective variables

    Generative modeling of conditional probability distributions on the level-sets of collective variables arXiv:2512.17374v1 Announce Type: new Abstract: Given a probability distribution $mu$ in $mathbb{R}^d$ represented by data, we study in this paper the generative modeling of its conditional probability distributions on the level-sets of a collective variable $xi: mathbb{R}^d rightarrow mathbb{R}^k$, where $1 le k…

  • Understanding the Generative AI User

    Understanding the Generative AI User What do regular technology users think (and know) about AI? The post Understanding the Generative AI User appeared first on Towards Data Science. Stephanie Kirmer Go to original source

  • Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI — Clearly Explained

    Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI — Clearly Explained Understanding AI in 2026 — from machine learning to generative models The post Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI — Clearly Explained appeared first on Towards Data Science. Sabrine Bendimerad Go to original source

  • Generative Modeling with Manifold Percolation

    Generative Modeling with Manifold Percolation arXiv:2511.20503v1 Announce Type: new Abstract: Generative modeling is typically framed as learning mapping rules, but from an observer’s perspective without access to these rules, the task manifests as disentangling the geometric support from the probability distribution. We propose that Continuum Percolation is uniquely suited for this support analysis, as the…

  • Generative AI Will Redesign Cars, But Not the Way Automakers Think

    Generative AI Will Redesign Cars, But Not the Way Automakers Think Traditional manufacturers are using revolutionary technology for incremental optimization instead of fundamental re-imagination The post Generative AI Will Redesign Cars, But Not the Way Automakers Think appeared first on Towards Data Science. Nishant Arora Go to original source

  • Friction on Demand: A Generative Framework for the Inverse Design of Metainterfaces

    Friction on Demand: A Generative Framework for the Inverse Design of Metainterfaces arXiv:2511.03735v1 Announce Type: new Abstract: Designing frictional interfaces to exhibit prescribed macroscopic behavior is a challenging inverse problem, made difficult by the non-uniqueness of solutions and the computational cost of contact simulations. Traditional approaches rely on heuristic search over low-dimensional parameterizations, which limits…

  • Generative Bayesian Optimization: Generative Models as Acquisition Functions

    Generative Bayesian Optimization: Generative Models as Acquisition Functions arXiv:2510.25240v1 Announce Type: new Abstract: We present a general strategy for turning generative models into candidate solution samplers for batch Bayesian optimization (BO). The use of generative models for BO enables large batch scaling as generative sampling, optimization of non-continuous design spaces, and high-dimensional and combinatorial design.…

  • Score-based constrained generative modeling via Langevin diffusions with boundary conditions

    Score-based constrained generative modeling via Langevin diffusions with boundary conditions arXiv:2510.23985v1 Announce Type: new Abstract: Score-based generative models based on stochastic differential equations (SDEs) achieve impressive performance in sampling from unknown distributions, but often fail to satisfy underlying constraints. We propose a constrained generative model using kinetic (underdamped) Langevin dynamics with specular reflection of velocity…

  • Calibrating Generative Models

    Calibrating Generative Models arXiv:2510.10020v1 Announce Type: new Abstract: Generative models frequently suffer miscalibration, wherein class probabilities and other statistics of the sampling distribution deviate from desired values. We frame calibration as a constrained optimization problem and seek the closest model in Kullback-Leibler divergence satisfying calibration constraints. To address the intractability of imposing these constraints exactly,…

  • Beyond Linear Diffusions: Improved Representations for Rare Conditional Generative Modeling

    Beyond Linear Diffusions: Improved Representations for Rare Conditional Generative Modeling arXiv:2510.02499v1 Announce Type: new Abstract: Diffusion models have emerged as powerful generative frameworks with widespread applications across machine learning and artificial intelligence systems. While current research has predominantly focused on linear diffusions, these approaches can face significant challenges when modeling a conditional distribution, $P(Y|X=x)$, when…

  • Generative AI Myths, Busted: An Engineers’s Quick Guide

    Generative AI Myths, Busted: An Engineers’s Quick Guide A super simple and quick guide to how generative AI works, the myths around it, and why it won’t replace engineers anytime soon. The post Generative AI Myths, Busted: An Engineers’s Quick Guide appeared first on Towards Data Science. Amy Ma Go to original source

  • DoubleGen: Debiased Generative Modeling of Counterfactuals

    DoubleGen: Debiased Generative Modeling of Counterfactuals arXiv:2509.16842v1 Announce Type: new Abstract: Generative models for counterfactual outcomes face two key sources of bias. Confounding bias arises when approaches fail to account for systematic differences between those who receive the intervention and those who do not. Misspecification bias arises when methods attempt to address confounding through estimation…

  • SURGIN: SURrogate-guided Generative INversion for subsurface multiphase flow with quantified uncertainty

    SURGIN: SURrogate-guided Generative INversion for subsurface multiphase flow with quantified uncertainty arXiv:2509.13189v1 Announce Type: new Abstract: We present a direct inverse modeling method named SURGIN, a SURrogate-guided Generative INversion framework tailed for subsurface multiphase flow data assimilation. Unlike existing inversion methods that require adaptation for each new observational configuration, SURGIN features a zero-shot conditional generation…

  • An invertible generative model for forward and inverse problems

    An invertible generative model for forward and inverse problems arXiv:2509.03910v1 Announce Type: new Abstract: We formulate the inverse problem in a Bayesian framework and aim to train a generative model that allows us to simulate (i.e., sample from the likelihood) and do inference (i.e., sample from the posterior). We review the use of triangular normalizing…

  • Weighted Support Points from Random Measures: An Interpretable Alternative for Generative Modeling

    Weighted Support Points from Random Measures: An Interpretable Alternative for Generative Modeling arXiv:2508.21255v1 Announce Type: new Abstract: Support points summarize a large dataset through a smaller set of representative points that can be used for data operations, such as Monte Carlo integration, without requiring access to the full dataset. In this sense, support points offer…

  • The Information Dynamics of Generative Diffusion

    The Information Dynamics of Generative Diffusion arXiv:2508.19897v1 Announce Type: new Abstract: Generative diffusion models have emerged as a powerful class of models in machine learning, yet a unified theoretical understanding of their operation is still developing. This perspective paper provides an integrated perspective on generative diffusion by connecting their dynamic, information-theoretic, and thermodynamic properties under…

  • Flow Matching-Based Generative Modeling for Efficient and Scalable Data Assimilation

    Flow Matching-Based Generative Modeling for Efficient and Scalable Data Assimilation arXiv:2508.13313v1 Announce Type: new Abstract: Data assimilation (DA) is the problem of sequentially estimating the state of a dynamical system from noisy observations. Recent advances in generative modeling have inspired new approaches to DA in high-dimensional nonlinear settings, especially the ensemble score filter (EnSF). However,…

  • A Malliavin calculus approach to score functions in diffusion generative models

    A Malliavin calculus approach to score functions in diffusion generative models arXiv:2507.05550v1 Announce Type: new Abstract: Score-based diffusion generative models have recently emerged as a powerful tool for modelling complex data distributions. These models aim at learning the score function, which defines a map from a known probability distribution to the target data distribution via…

  • With Generative AI looking so ominous, would there be any further research in any other domains like Computer Vision or NLP or Graph Analytics ever?

    With Generative AI looking so ominous, would there be any further research in any other domains like Computer Vision or NLP or Graph Analytics ever? So as the title suggest, last few years have been just Generative AI all over the place. Every new research is somehow focussed towards it. So does this mean other…

  • Enhancing Interpretability in Generative Modeling: Statistically Disentangled Latent Spaces Guided by Generative Factors in Scientific Datasets

    Enhancing Interpretability in Generative Modeling: Statistically Disentangled Latent Spaces Guided by Generative Factors in Scientific Datasets arXiv:2507.00298v1 Announce Type: new Abstract: This study addresses the challenge of statistically extracting generative factors from complex, high-dimensional datasets in unsupervised or semi-supervised settings. We investigate encoder-decoder-based generative models for nonlinear dimensionality reduction, focusing on disentangling low-dimensional latent variables…

  • A comparison of generative deep learning methods for multivariate angular simulation

    A comparison of generative deep learning methods for multivariate angular simulation arXiv:2504.21505v1 Announce Type: new Abstract: With the recent development of new geometric and angular-radial frameworks for multivariate extremes, reliably simulating from angular variables in moderate-to-high dimensions is of increasing importance. Empirical approaches have the benefit of simplicity, and work reasonably well in low dimensions,…

  • No-Regret Generative Modeling via Parabolic Monge-Amp`ere PDE

    No-Regret Generative Modeling via Parabolic Monge-Amp`ere PDE arXiv:2504.09279v1 Announce Type: new Abstract: We introduce a novel generative modeling framework based on a discretized parabolic Monge-Amp`ere PDE, which emerges as a continuous limit of the Sinkhorn algorithm commonly used in optimal transport. Our method performs iterative refinement in the space of Brenier maps using a mirror…

  • Understanding the Tech Stack Behind Generative AI

    Understanding the Tech Stack Behind Generative AI Understanding the Tech Stack Behind Generative AI When ChatGPT reached the one million user mark within five days and took off faster than any other technology in history, the world began to pay attention to artificial intelligence and AI applications. And so it continued apace. Since then, many…

  • Generative AI Is Declarative

    Generative AI Is Declarative ChatGPT launched in 2022 and kicked off the Generative Ai boom. In the two years since, academics, technologists, and armchair experts have written libraries worth of articles on the technical underpinnings of generative AI and about the potential capabilities of both current and future generative AI models. Surprisingly little has been…

  • Generative Distribution Prediction: A Unified Approach to Multimodal Learning

    Generative Distribution Prediction: A Unified Approach to Multimodal Learning arXiv:2502.07090v1 Announce Type: new Abstract: Accurate prediction with multimodal data-encompassing tabular, textual, and visual inputs or outputs-is fundamental to advancing analytics in diverse application domains. Traditional approaches often struggle to integrate heterogeneous data types while maintaining high predictive accuracy. We introduce Generative Distribution Prediction (GDP), a…

  • Trustworthy Evaluation of Generative AI Models

    Trustworthy Evaluation of Generative AI Models arXiv:2501.18897v1 Announce Type: new Abstract: Generative AI (GenAI) models have recently achieved remarkable empirical performance in various applications, however, their evaluations yet lack uncertainty quantification. In this paper, we propose a method to compare two generative models based on an unbiased estimator of their relative performance gap. Statistically, our…

  • The Cultural Backlash Against Generative AI

    The Cultural Backlash Against Generative AI What’s making many people resent generative AI, and what impact does that have on the companies responsible? Photo by Joshua Hoehne on Unsplash The recent reveal of DeepSeek-R1, the large scale LLM developed by a Chinese company (also named DeepSeek), has been a very interesting event for those of us…

  • Why Generative-AI Apps’ Quality Often Sucks and What to Do About It

    Why Generative-AI Apps’ Quality Often Sucks and What to Do About It How to get from PoCs to tested high-quality applications in production Image licensed from elements.envato.com, edit by Marcel Müller, 2025 The generative AI hype has rolled through the business world in the past two years. This technology can make business process executions more efficient,…

  • Generative Models with ELBOs Converging to Entropy Sums

    Generative Models with ELBOs Converging to Entropy Sums arXiv:2501.09022v1 Announce Type: new Abstract: The evidence lower bound (ELBO) is one of the most central objectives for probabilistic unsupervised learning. For the ELBOs of several generative models and model classes, we here prove convergence to entropy sums. As one result, we provide a list of generative…

  • On the Statistical Capacity of Deep Generative Models

    On the Statistical Capacity of Deep Generative Models arXiv:2501.07763v1 Announce Type: new Abstract: Deep generative models are routinely used in generating samples from complex, high-dimensional distributions. Despite their apparent successes, their statistical properties are not well understood. A common assumption is that with enough training data and sufficiently large neural networks, deep generative model samples…

  • A Distributional Evaluation of Generative Image Models

    A Distributional Evaluation of Generative Image Models arXiv:2501.00744v1 Announce Type: new Abstract: Generative models are ubiquitous in modern artificial intelligence (AI) applications. Recent advances have led to a variety of generative modeling approaches that are capable of synthesizing highly realistic samples. Despite these developments, evaluating the distributional match between the synthetic samples and the target…

  • GDD: Generative Driven Design

    GDD: Generative Driven Design Reflective generative AI software components as a development paradigm Nowhere has the proliferation of generative AI tooling been more aggressive than in the world of software development. It began with GitHub Copilot’s supercharged autocomplete, then exploded into direct code-along integrated tools like Aider and Cursor that allow software engineers to dictate…

  • Generative Modeling with Diffusion

    Generative Modeling with Diffusion arXiv:2412.10948v1 Announce Type: new Abstract: We introduce the diffusion model as a method to generate new samples. Generative models have been recently adopted for tasks such as art generation (Stable Diffusion, Dall-E) and text generation (ChatGPT). Diffusion models in particular apply noise to sample data and then “reverse” this noising process…

  • Why Internal Company Chatbots Fail and How to Use Generative AI in Enterprise with Impact

    Why Internal Company Chatbots Fail and How to Use Generative AI in Enterprise with Impact Start with the problem and not with the solution Background licensed from elements.envato.com, edit by Marcel Müller 2024 The most common disillusion that many organizations have is the following: They get excited about generative AI with ChatGPT or Microsoft Co-Pilot, read some…