Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Podstawski, Michał"'
Autor:
Besta, Maciej, Kubicek, Ales, Niggli, Roman, Gerstenberger, Robert, Weitzendorf, Lucas, Chi, Mingyuan, Iff, Patrick, Gajda, Joanna, Nyczyk, Piotr, Müller, Jürgen, Niewiadomski, Hubert, Chrapek, Marcin, Podstawski, Michał, Hoefler, Torsten
Retrieval Augmented Generation (RAG) enhances the abilities of Large Language Models (LLMs) by enabling the retrieval of documents into the LLM context to provide more accurate and relevant responses. Existing RAG solutions do not focus on queries th
Externí odkaz:
http://arxiv.org/abs/2406.05085
Autor:
Besta, Maciej, Blach, Nils, Kubicek, Ales, Gerstenberger, Robert, Podstawski, Michal, Gianinazzi, Lukas, Gajda, Joanna, Lehmann, Tomasz, Niewiadomski, Hubert, Nyczyk, Piotr, Hoefler, Torsten
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence 2024 (AAAI'24)
We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-of-Thought or Tree of Thoughts (ToT). The key idea and primary advantage of GoT is
Externí odkaz:
http://arxiv.org/abs/2308.09687
Autor:
Besta, Maciej, Gerstenberger, Robert, Fischer, Marc, Podstawski, Michał, Blach, Nils, Egeli, Berke, Mitenkov, Georgy, Chlapek, Wojciech, Michalewicz, Marek, Niewiadomski, Hubert, Müller, Jürgen, Hoefler, Torsten
Publikováno v:
Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, 2023 (SC '23)
Graph databases (GDBs) are crucial in academic and industry applications. The key challenges in developing GDBs are achieving high performance, scalability, programmability, and portability. To tackle these challenges, we harness established practice
Externí odkaz:
http://arxiv.org/abs/2305.11162
Autor:
Besta, Maciej, Iff, Patrick, Scheidl, Florian, Osawa, Kazuki, Dryden, Nikoli, Podstawski, Michal, Chen, Tiancheng, Hoefler, Torsten
Publikováno v:
Learning on Graphs (LOG) 2022
Graph databases (GDBs) enable processing and analysis of unstructured, complex, rich, and usually vast graph datasets. Despite the large significance of GDBs in both academia and industry, little effort has been made into integrating them with the pr
Externí odkaz:
http://arxiv.org/abs/2209.09732
Autor:
Besta, Maciej, Miglioli, Cesare, Labini, Paolo Sylos, Tětek, Jakub, Iff, Patrick, Kanakagiri, Raghavendra, Ashkboos, Saleh, Janda, Kacper, Podstawski, Michal, Kwasniewski, Grzegorz, Gleinig, Niels, Vella, Flavio, Mutlu, Onur, Hoefler, Torsten
Publikováno v:
Proceedings of the ACM/IEEE International Conference on High Performance Computing, Networking, Storage and Analysis, November 2022
Important graph mining problems such as Clustering are computationally demanding. To significantly accelerate these problems, we propose ProbGraph: a graph representation that enables simple and fast approximate parallel graph mining with strong theo
Externí odkaz:
http://arxiv.org/abs/2208.11469
Function-as-a-Service (FaaS) is one of the most promising directions for the future of cloud services, and serverless functions have immediately become a new middleware for building scalable and cost-efficient microservices and applications. However,
Externí odkaz:
http://arxiv.org/abs/2012.14132
Publikováno v:
Proceedings of the 26th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC'17), 2017
We reduce the cost of communication and synchronization in graph processing by analyzing the fastest way to process graphs: pushing the updates to a shared state or pulling the updates to a private state.We investigate the applicability of this push-
Externí odkaz:
http://arxiv.org/abs/2010.16012
Autor:
Besta, Maciej, Gerstenberger, Robert, Peter, Emanuel, Fischer, Marc, Podstawski, Michał, Barthels, Claude, Alonso, Gustavo, Hoefler, Torsten
Graph processing has become an important part of multiple areas of computer science, such as machine learning, computational sciences, medical applications, social network analysis, and many others. Numerous graphs such as web or social networks may
Externí odkaz:
http://arxiv.org/abs/1910.09017
Akademický článek
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Autor:
Besta, Maciej, Gerstenberger, Robert, Fischer, Marc, Podstawski, Michał, Müller, Jürgen, Blach, Nils, Egeli, Berke, Mitenkov, George, Chlapek, Wojciech, Michalewicz, Marek, Hoefler, Torsten
Graph databases (GDBs) are crucial in academic and industry applications. The key challenges in developing GDBs are achieving high performance, scalability, programmability, and portability. To tackle these challenges, we harness established practice
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::86709abb2df4c136a77a745f5605eb89