Taking a quantum leap in time to solution for simulations of high-Tc superconductors. - Peter W. J. Staar; Thomas A. Maier; Michael S. Summers; Gilles Fourestey; Raffaele Solcà; Thomas C. Schulthess (SC, 2013)

An extreme-scale implicit solver for complex PDEs: highly heterogeneous flow in earth's mantle. - Johann Rudi; A. Cristiano I. Malossi; Tobin Isaac; Georg Stadler; Michael Gurnis; Peter W. J. Staar; Yves Ineichen; Costas Bekas; Alessandro Curioni; Omar Ghattas (SC, 2015)

Energy-efficient stochastic matrix function estimator for graph analytics on FPGA. - Heiner Giefers; Peter W. J. Staar; Raphael Polig (FPL, 2016)

Stochastic Matrix-Function Estimators: Scalable Big-Data Kernels with High Performance. - Peter W. J. Staar; Panagiotis Kl. Barkoutsos; Roxana Istrate; A. Cristiano I. Malossi; Ivano Tavernelli; Nikolaj Moll; Heiner Giefers; Christoph Hagleitner; Costas Bekas; Alessandro Curioni (IPDPS, 2016)

Analyzing the energy-efficiency of sparse matrix multiplication on heterogeneous systems: A comparative study of GPU, Xeon Phi and FPGA. - Heiner Giefers; Peter W. J. Staar; Costas Bekas; Christoph Hagleitner (ISPASS, 2016)

NanoStreams: Codesigned microservers for edge analytics in real time. - Giorgis Georgakoudis; Charles Gillan; Ahmad Hassan; Umar Ibrahim Minhas; Ivor T. A. Spence; George Tzenakis; Hans Vandierendonck; Roger F. Woods; Dimitrios S. Nikolopoulos; Murali Shyamsundar; Paul Barber; Matthew Russell; et al. (SAMOS, 2016)

Exploring Graph Analytics with the PCJ Toolbox. - Roxana Istrate; Panagiotis Kl. Barkoutsos; Michele Dolfi; Peter W. J. Staar; Costas Bekas (PPAM (2), 2017)

Corpus Conversion Service: A Machine Learning Platform to Ingest Documents at Scale. - Peter W. J. Staar; Michele Dolfi; Christoph Auer; Costas Bekas (KDD, 2018)

Corpus Conversion Service: A machine learning platform to ingest documents at scale [Poster abstract]. - Peter W. J. Staar; Michele Dolfi; Christoph Auer; Costas Bekas (CoRR, 2018)

An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries. - Matteo Manica; Christoph Auer; Valéry Weber; Federico Zipoli; Michele Dolfi; Peter W. J. Staar; Teodoro Laino; Costas Bekas; Akihiro Fujita; Hiroki Toda; Shuichi Hirose; Yasumitsu Orii (CoRR, 2019)

DCA++: A software framework to solve correlated electron problems with modern quantum cluster methods. - Urs R. Hähner; Gonzalo Alvarez 001; Thomas A. Maier; Raffaele Solcà; Peter W. J. Staar; Michael S. Summers; Thomas C. Schulthess (Comput. Phys. Commun., 2020)

Robust PDF Document Conversion using Recurrent Neural Networks. - Nikolaos Livathinos; Cesar Berrospi; Maksym Lysak; Viktor Kuropiatnyk; Ahmed S. Nassar; André Carvalho; Michele Dolfi; Christoph Auer; Kasper Dinkla; Peter W. J. Staar (AAAI, 2021)

Racial Representation Analysis in Dermatology Academic Materials. - Girmaw Abebe Tadesse; Celia Cintas; Roxana Daneshjou; Kush R. Varshney; Peter W. J. Staar; Skyler Speakman; Kenya S. Andrews; Chinyere Agunwa; Justin Jia; Elizabeth E. Bailey; Jules Lipoff; Ginikanwa Onyekaba; et al. (AMIA, 2021)

Delivering Document Conversion as a Cloud Service with High Throughput and Responsiveness. - Christoph Auer; Michele Dolfi; André Carvalho; Cesar Berrospi Ramis; Peter W. J. Staar (CLOUD, 2022)

Unsupervised Domain Generalization by Learning a Bridge Across Domains. - Sivan Harary; Eli Schwartz; Assaf Arbelle; Peter W. J. Staar; Shady Abu-Hussein; Elad Amrani; Roei Herzig; Amit Alfassy; Raja Giryes; Hilde Kuehne; Dina Katabi; Kate Saenko; et al. (CVPR, 2022)

TableFormer: Table Structure Understanding with Transformers. - Ahmed S. Nassar; Nikolaos Livathinos; Maksym Lysak; Peter W. J. Staar (CVPR, 2022)

Unsupervised Term Extraction for Highly Technical Domains. - Francesco Fusco; Peter W. J. Staar; Diego Antognini (EMNLP (Industry Track), 2022)

DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation. - Birgit Pfitzmann; Christoph Auer; Michele Dolfi; Ahmed S. Nassar; Peter W. J. Staar (KDD, 2022)

FETA: Towards Specializing Foundational Models for Expert Task Applications. - Amit Alfassy; Assaf Arbelle; Oshri Halimi; Sivan Harary; Roei Herzig; Eli Schwartz; Rameswar Panda; Michele Dolfi; Christoph Auer; Peter W. J. Staar; Kate Saenko; Rogério Feris; et al. (NeurIPS, 2022)

pNLP-Mixer: an Efficient all-MLP Architecture for Language. - Francesco Fusco; Damian Pascual; Peter W. J. Staar (CoRR, 2022)

DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis. - Birgit Pfitzmann; Christoph Auer; Michele Dolfi; Ahmed S. Nassar; Peter W. J. Staar (CoRR, 2022)

BusiNet - a Light and Fast Text Detection Network for Business Documents. - Oshri Naparstek; Ophir Azulai; Daniel Rotman; Yevgeny Burshtein; Peter W. J. Staar; Udi Barzelay (CoRR, 2022)

FETA: Towards Specializing Foundation Models for Expert Task Applications. - Amit Alfassy; Assaf Arbelle; Oshri Halimi; Sivan Harary; Roei Herzig; Eli Schwartz; Rameswar Panda; Michele Dolfi; Christoph Auer; Kate Saenko; Peter W. J. Staar; Rogério Feris; et al. (CoRR, 2022)

pNLP-Mixer: an Efficient all-MLP Architecture for Language. - Francesco Fusco; Damian Pascual; Peter W. J. Staar; Diego Antognini (ACL (industry), 2023)

MolGrapher: Graph-based Visual Recognition of Chemical Structures. - Lucas Morin; Martin Danelljan; Maria Isabel Agea; Ahmed S. Nassar; Valéry Weber; Ingmar Meijer; Peter W. J. Staar; Fisher Yu 001 (ICCV, 2023)

ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents. - Christoph Auer; Ahmed S. Nassar; Maksym Lysak; Michele Dolfi; Nikolaos Livathinos; Peter W. J. Staar (ICDAR (2), 2023)

Optimized Table Tokenization for Table Structure Recognition. - Maksym Lysak; Ahmed S. Nassar; Nikolaos Livathinos; Christoph Auer; Peter W. J. Staar (ICDAR (2), 2023)

Skin Tone Analysis for Representation in Educational Materials (STAR-ED) using machine learning. - Girmaw Abebe Tadesse; Celia Cintas; Kush R. Varshney; Peter W. J. Staar; Chinyere Agunwa; Skyler Speakman; Justin Jia; Elizabeth E. Bailey; Ademide Adelekun; Jules Lipoff; Ginikanwa Onyekaba; Jenna C. Lester; et al. (npj Digit. Medicine, 2023)

ESG Accountability Made Easy: DocQA at Your Service. - Lokesh Mishra; Cesar Berrospi; Kasper Dinkla; Diego Antognini; Francesco Fusco; Benedikt Bothur; Maksym Lysak; Nikolaos Livathinos; Ahmed S. Nassar; Panagiotis Vagenas; Lucas Morin; Christoph Auer; et al. (AAAI, 2024)

Clustering Items From Adaptively Collected Inconsistent Feedback. - Shubham Gupta; Peter W. J. Staar; Christian de Sainte Marie (AISTATS, 2024)

INDUS: Effective and Efficient Language Models for Scientific Applications. - Bishwaranjan Bhattacharjee; Aashka Trivedi; Masayasu Muraoka; Muthukumaran Ramasubramanian; Takuma Udagawa; Iksha Gurung; Nishan Pantha; Rong Zhang; Bharath Dandala; Rahul Ramachandran; Manil Maskey; Kaylin M. Bugbee; et al. (EMNLP (Industry Track), 2024)

KVP10k : A Comprehensive Dataset for Key-Value Pair Extraction in Business Documents. - Oshri Naparstek; Ophir Azulai; Inbar Shapira; Elad Amrani; Yevgeny Yaroker; Yevgeny Burshtein; Roi Pony; Nadav Rubinstein; Foad Abo Dahood; Orit Prince; Idan Friedman; Christoph Auer; et al. (ICDAR (1), 2024)

KVP10k : A Comprehensive Dataset for Key-Value Pair Extraction in Business Documents. - Oshri Naparstek; Roi Pony; Inbar Shapira; Foad Abo Dahood; Ophir Azulai; Yevgeny Yaroker; Nadav Rubinstein; Maksym Lysak; Peter W. J. Staar; Ahmed Nassar; Nikolaos Livathinos; Christoph Auer; et al. (CoRR, 2024)

INDUS: Effective and Efficient Language Models for Scientific Applications. - Bishwaranjan Bhattacharjee; Aashka Trivedi; Masayasu Muraoka; Muthukumaran Ramasubramanian; Takuma Udagawa; Iksha Gurung; Rong Zhang; Bharath Dandala; Rahul Ramachandran; Manil Maskey; Kaylin M. Bugbee; Mike Little; et al. (CoRR, 2024)

Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs. - Lokesh Mishra; Sohayl Dhibi; Yusik Kim; Cesar Berrospi Ramis; Shubham Gupta; Michele Dolfi; Peter W. J. Staar (CoRR, 2024)

Docling Technical Report. - Christoph Auer; Maksym Lysak; Ahmed S. Nassar; Michele Dolfi; Nikolaos Livathinos; Panos Vagenas; Cesar Berrospi Ramis; Matteo Omenetti; Fabian Lindlbauer; Kasper Dinkla; Lokesh Mishra; Yusik Kim; et al. (CoRR, 2024)

Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems. - Rafael Teixeira de Lima; Shubham Gupta; Cesar Berrospi Ramis; Lokesh Mishra; Michele Dolfi; Peter W. J. Staar; Panagiotis Vagenas (COLING (Industry), 2025)

MarkushGrapher: Joint Visual and Textual Recognition of Markush Structures. - Lucas Morin; Valéry Weber; Ahmed Nassar; Gerhard Ingmar Meijer; Luc Van Gool; Yawei Li 001; Peter W. J. Staar (CVPR, 2025)

Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion. - Nikolaos Livathinos; Christoph Auer; Maksym Lysak; Ahmed Nassar; Michele Dolfi; Panos Vagenas; Cesar Berrospi Ramis; Matteo Omenetti; Kasper Dinkla; Yusik Kim; Shubham Gupta; Rafael Teixeira de Lima; et al. (CoRR, 2025)

Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence. - Leonid Karlinsky; Assaf Arbelle; Abraham Daniels; Ahmed Nassar; Amit Alfassi; Bo Wu 018; Eli Schwartz; Dhiraj Joshi; Jovana Kondic; Nimrod Shabtay; Pengyuan Li 001; Roei Herzig; et al. (CoRR, 2025)

SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion. - Ahmed Nassar; Andrés Marafioti; Matteo Omenetti; Maksym Lysak; Nikolaos Livathinos; Christoph Auer; Lucas Morin; Rafael Teixeira de Lima; Yusik Kim; A. Said Gurbuz; Michele Dolfi; Miquel Farré; et al. (CoRR, 2025)

MarkushGrapher: Joint Visual and Textual Recognition of Markush Structures. - Lucas Morin; Valéry Weber; Ahmed S. Nassar; Gerhard Ingmar Meijer; Luc Van Gool; Yawei Li 001; Peter W. J. Staar (CoRR, 2025)

Advanced Layout Analysis Models for Docling. - Nikolaos Livathinos; Christoph Auer; Ahmed S. Nassar; Rafael Teixeira de Lima; Maksym Lysak; Brown Ebouky; Cesar Berrospi; Michele Dolfi; Panagiotis Vagenas; Matteo Omenetti; Kasper Dinkla; Yusik Kim; et al. (CoRR, 2025)

Subgrapher: visual fingerprinting of chemical structures. - Lucas Morin; Gerhard Ingmar Meijer; Valéry Weber; Luc Van Gool; Peter W. J. Staar (J. Cheminformatics, 2025)