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 Computation and Language
Sep 22
A framework for recipe data structure with applications for culinary and nutritional insights
arXiv:2609.22099v1 Announce Type: new Abstract: Cooking is a complex process that transforms raw ingredients into delicious and nutritious dishes, yet the recipes that encode this process remain larg...
arXiv Machine Learning
Aug 27
A General-Purpose Framework for Chemical Reaction Representation with Atomic Correspondence and Flexible Condition Adaptation
The paper introduces Align-React, a chemical reaction representation learning framework that incorporates atomic correspondence between reactants and products, an adapter for embedding reaction conditions, and a Reaction-Center-Aware attention mechanism. These components enable the model to capture precise molecular transformations and focus on critical functional groups, leading to improved performance across a variety of organic reaction tasks. The framework outperforms existing architectures on most benchmark datasets.
arXiv AI
Aug 17
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 Machine Learning
1d ago
RelICL: Training-free Relational Learning with Tabular Foundation Models
RelICL: Training-free Relational Learning with Tabular Foundation Models proposes a new method for relational learning that addresses two key issues of deep feature synthesis—feature explosion and interaction blindness—by propagating and fusing information step by step through the schema graph using a tabular foundation model. The approach retains the benefits of DFS while improving scalability and performance. Experiments on RelBench tasks show that RelICL performs on par with the strongest DFS-based approach.
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 AI
4d ago
LoopICL: Looping a single transformer block to solve tabular tasks
LoopICL is a transformer architecture that loops a single block to address tabular tasks. It separates parameter count from computational depth by using a cell stream for per‑cell features and a row stream for in‑context examples, refined via within‑column and cross‑column attention. During pre‑training, varying loop counts and a learned exit‑gate allow the model to adjust inference depth at test time, achieving competitive performance with TabICLv2 while using about 90% fewer parameters.
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
Sep 4
SimpleDesign: A Joint Model for Protein Sequence and Structure Codesign
SimpleDesign is a single-stage, end-to-end model for joint protein sequence and structure design that eliminates the need for multi-stage training. It combines discrete cross-entropy for sequences with a regression objective for structures, using a Mixture-of-Transformer architecture to handle modality-specific processing while maintaining global self-attention. Trained on over 2 million sequence-structure pairs, SimpleDesign achieves strong performance on co-design and unconditional generation benchmarks.
arXiv AI
Sep 11
Hierarchical and Permutation-Invariant Feature Transformation Learning via Policy-Guided Embedding Search
The paper introduces a framework for learning feature transformations on tabular data that addresses three limitations of prior generative methods: neglect of hierarchical feature relationships, bias from order-sensitive embeddings, and reliance on gradient-based search. It combines a permutation‑invariant hierarchical module using self‑attention pooling to capture interactions across features, operations, and abstraction levels, with a policy‑guided multi‑objective reinforcement learning strategy that starts from strong seeds and optimizes both predictive accuracy and transformation efficiency. Experiments on diverse tabular benchmarks show the approach outperforms strong baselines, and the authors provide public code and data.