25 papers · ranked by Valyu relevance
Hu Xin, Xu Pengfei, Zhou Jin, Fu Hongbo + 1 more
—Structured layouts are preferable in many 2D visual contents (e.g., GUIs, webpages) since the structural information allows convenient layout editing. Computational frameworks can help create structured layouts but require heavy labor input. Existing data-driven approaches are effective in automatically generating…
Hu Xin, Xu Pengfei, Zhou Jin, Fu Hongbo + 1 more
—Structured layouts are preferable in many 2D visual contents (e.g., GUIs, webpages) since the structural information allows convenient layout editing. Computational frameworks can help create structured layouts but require heavy labor input. Existing data-driven approaches are effective in automatically generating…
Ashraf Elnashar, Jules White, Douglas C. Schmidt
Large language models (LLMs), such as GPT-4o, provide versatile techniques for generating and formatting structured data. However, prompt style plays a critical role in determining the accuracy, efficiency, and token cost of the generated outputs. This paper explores the effectiveness of three specific prompt…
Zeyu Fu
Generating realistic single-cell transcriptomic profiles from structured biological descriptions would enable controlled simulation, data augmentation, and hypothesis-driven cell-state creation—yet no existing method combines text–cell alignment with conditional generation. We present CLOP-DiT, a modular three-stage…
Alessio Barboni, Massimiliano Lupo Pasini, Bishal Lakha, Edoardo Serra
Generating realistic and diverse graphs is a key problem in machine learning, with applications in molecular discovery, circuit design, cybersecurity, and beyond. However, current graph generative models remain limited by scalability and novelty. Diffusion-based methods often require costly full-adjacency operations…
Yafu Li, Leyang Cui, Jianhao Yan, Yongjng Yin + 3 more
'Yue Zhang'] Most existing text generation models follow the sequence-to-sequence paradigm. Generative Grammar suggests that humans generate natural language texts by learning language grammar. We propose a syntax-guided generation schema, which generates the sequence guided by a constituency parse tree in a topdown…
Uğur, Özgür, Yılmaz, Musa + 10 more
—The integration of Large Language Models (LLMs) into various applications has driven the need for structured and reliable responses. A key challenge in Retrieval-Augmented Generation (RAG) systems is ensuring that outputs align with expected formats while minimizing hallucinations. This study examines the role of…
Edgardo Samuel Barraza Verdesoto, Marlly Yaneth Rojas Ortiz, Richard de Jesus Gil Herrera
'Richard de Jesus Gil Herrera'] This paper introduces a system that incorporates several strategies based on scientific models of how the brain records and recovers memories. Methodologically, an incremental prototyping approach has been applied to develop a satisfactory architecture that can be adapted to any…
Lazlo Bleker, Zifeng Guo, Kaleb Smith, Kam-Ming Mark Tam + 2 more
This paper presents Text2Structure3D, a graph-based Machine Learning (ML) model that generates equilibrium structures from natural language prompts. Text2Structure3D is designed to support new intuitive ways of design exploration and iteration in the conceptual structural design process. The approach combines latent…
Shuang Zheng, Zhenyu Zhao, Weifeng Zhang
Phase-structured light beams carrying orbital angular momentum (OAM) have a wide range of applications ranging from particle trapping to optical communication. Many techniques exist to generate and manipulate such beams but most suffer from bulky configurations. In contrast, silicon photonics enables the integration of…
Authors not listed
The ability to generate crystal structures directly from textual descriptions marks a pivotal advancement in materials informatics and underscores the emerging role of large language models (LLMs) in inverse design. In this work, we introduce CrysText, a text-conditioned framework that generates crystal structures in…
Rıza Özçelik, Sarah de Ruiter, Francesca Grisoni
Generative deep learning is reshaping drug design. Chemical language models (CLMs) – which generate molecules in the form of molecular strings – bear particular promise for this endeavor. Here, we introduce a recent deep learning architecture, termed Structured State-Space Sequence (S4) model, into de novo drug design.…
Michael Statt, Kristopher Brown, Santosh Suram, Linda Hung + 3 more
In this work, we present DBgen, a Python library that provides a framework for defining extract-transform-load (ETL) pipelines to create and populate SQL databases. DBgen is most useful when the underlying data has complex relationships, requires multi-step analysis, is large-scale, and the type of data being collected…
Ran He, Jie Cao, Tieniu Tan
Generative artificial intelligence (GAI) has recently achieved significant success, enabling anyone to create texts, images, videos and even computer codes while providing insights that might not be possible with traditional tools. To stimulate future research, this work provides a brief summary of the ongoing and…
David M. Schmidt, Philipp Cimiano
Background In the field of structured information extraction, there are typically semantic and syntactic constraints on the output of information extraction (IE) systems. These constraints, however, can typically not be guaranteed using standard (fine-tuned) encoder-decoder architectures. This has led to the…
Pengzhi Huang, François Charton, Jan-Niklas M. Schmelzle, Shelby S. Darnell + 3 more
Language Models (LM) have been extensively utilized for learning DNA sequence patterns and generating synthetic sequences. In this paper, we present a novel approach for the generation of synthetic DNA data using pangenomes in combination with LM. We introduce three innovative pangenome-based tokenization schemes that…
Frederic Abraham, Matthew Stephenson
This paper investigates the suitability of using Generative Adversarial Networks (GANs) to generate stable structures for the physics-based puzzle game Angry Birds. While previous applications of GANs for level generation have been mostly limited to tile-based representations, this paper explores their suitability for…
Markus E Nebel, Anika Scheid, Frank Weinberg
Background Random biological sequences are a topic of great interest in genome analysis since, according to a powerful paradigm, they represent the background noise from which the actual biological information must differentiate. Accordingly, the generation of random sequences has been investigated for a long time.…
Sanjar Adilov
Generative neural networks have shown promising results in de novo drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this…
Authors not listed
Computational methods for generating molecules with specific physiochemical properties or biolog- ical activity can greatly assist drug discovery efforts. Deep learning generative models constitute a significant step towards that direction. In this work, we introduce a novel approach that utilizes a Reinforcement…
Wen Xing, Juan Yang
This study presents the development and evaluation of a novel GPT-like conditional molecule generator designed to optimize the synthesis of chemical compounds with desirable properties. The model incorporates six pivotal physicochemical properties as conditions: molecular weight, number of non-hydrogen atoms, ring…
Zichen Zhang, Wanheng Zhang, Xiaochen Yang, Yujue Li + 3 more
Genetic evidence is a major determinant of clinical success in drug development, yet its aggregation has long relied on laborious human curation. Large language models (LLMs) have the potential to rapidly synthesize knowledge across biomedical resources, providing a route to scalable AI-driven genetic evidence…
Authors not listed
In recent years, generative deep learning has emerged as a transformative approach in drug design, promising to explore the vast chemical space and generate novel molecules with desired biological properties. This perspective examines the challenges and opportunities of applying generative models to drug discovery…
Authors not listed
Digital twins are virtual companions for the design, scale-up, and control of chemical processes. Equipping digital twins with mechanistic models of their mirrored unit operation expands their range of applicability compared to pure data-driven models. As constructing mechanistic models requires time, effort, and…
Alice Lacan, Romain André, Michele Sebag, Blaise Hanczar
RNA-seq data is used for precision medicine (e.g., cancer predictions), which benefits from deep learning approaches to analyze complex gene expression data. However, transcriptomics datasets often have few samples compared to deep learning standards. Synthetic data generation is thus being explored to address this…