arXiv AI
Aug 10
ReGraph: Learning to Generate Recipe Graphs from Food Images
arXiv:2608. 06917v1 Announce Type: new Abstract: Recent Large Multimodal Models (LMMs) have achieved impressive performance in recipe generation from food images.
RecipeNet: A Hierarchical Transformer for Recipe Data
arXiv:2608. 14505v1 Announce Type: cross Abstract: Recipe data arises in domains such as materials synthesis, pharmaceutical formulation, and industrial manufacturing, where procedures are represented as ordered sequences of steps containing heterogeneous structured fields.
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arXiv AI
2d ago
Generating Benchmark Health Data Using a Tabular Diffusion Transformer
arXiv:2608. 14496v1 Announce Type: cross Abstract: Cross-Tabular Data Generation (CTDG) seeks to learn a generative model from multiple heterogeneous tables and produce new synthetic tabular datasets.
arXiv Machine Learning
Jul 15
Hierarchical Synthetic Tabular Data Generation: A Hybrid Top-Down and Bottom-Up Framework
arXiv:2605. 28198v2 Announce Type: replace Abstract: Existing approaches for synthetic tabular data generation are based on either purely generative models or LLMs, both of which struggle with data heterogeneity, logical consistency, rare-event coverage, and robustness in low-data regimes.
arXiv AI
Jun 30
Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Framework
arXiv:2406. 08311v3 Announce Type: replace-cross Abstract: Existing evaluations of tabular synthesis models rely primarily on low-order statistics and downstream task performance, leaving multivariate causal relationships that go beyond pairwise correlations largely unmeasured.
arXiv Machine Learning
Jun 15
LapidaryEngine: Fully Conversational Crystal Generation
arXiv:2606. 14215v1 Announce Type: new Abstract: The emergence of Large Language Models (LLMs) has inspired the vision of generating bespoke crystal materials directly from natural-language instructions, enabling users to design materials through intuitive, conversational interaction.
arXiv AI
Jul 28
Procedural Content Generation via Generative Artificial Intelligence
arXiv:2407. 09013v2 Announce Type: replace Abstract: The attempt to utilize machine learning in PCG has been made in the past.
arXiv Machine Learning
Jul 15
Learning Mechanistic Reasoning for Chemical Reactions with Large Language Models
arXiv:2607. 12771v1 Announce Type: new Abstract: Reaction mechanisms consist of the step-by-step sequences of elementary reactions that explain chemical transformations.
arXiv Machine Learning
Jun 9
Learning from flowsheets: A generative transformer model for autocompletion of flowsheets
arXiv:2208. 00859v2 Announce Type: replace Abstract: We propose a novel method enabling autocompletion of chemical flowsheets.
arXiv AI
Jul 21
SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift
arXiv:2607. 18072v1 Announce Type: cross Abstract: Generative models trained on a source domain often produce samples that are poorly aligned with shifted target domains, limiting their effectiveness for target-domain data augmentation.
arXiv Machine Learning
22h ago
TabNSM: Neural Sparse Mixer for Tabular Regression
arXiv:2608. 18026v1 Announce Type: new Abstract: Large-scale, high-dimensional tabular regression remains challenging: tree-based models are robust but lack end-to-end representation learning, while deep models enable flexible feature learning but often incur costly interaction modeling and sensitivity to noisy or redundant features.
arXiv Machine Learning
Jun 2
Segment-driven Structural Induction and Semantic Alignment for Heterogeneous Tabular Representation
arXiv:2606. 01890v1 Announce Type: new Abstract: Real-world domains often contain heterogeneous tables whose headers vary while their underlying attribute semantics are shared, making it difficult to induce domain-specialized semantics from table-local evidence alone.
arXiv AI
Jun 10
Scone: Bridging Composition and Distinction in Subject-Driven Image Generation via Unified Understanding-Generation Modeling
arXiv:2512. 12675v3 Announce Type: replace-cross Abstract: Subject-driven image generation has advanced from single- to multi-subject composition, while neglecting distinction, the ability to distinguish and generate the correct subject when inputs contain multiple candidates.