Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Mathias Niepert"'
Autor:
Viktor Zaverkin, David Holzmüller, Henrik Christiansen, Federico Errica, Francesco Alesiani, Makoto Takamoto, Mathias Niepert, Johannes Kästner
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-18 (2024)
Abstract Efficiently creating a concise but comprehensive data set for training machine-learned interatomic potentials (MLIPs) is an under-explored problem. Active learning, which uses biased or unbiased molecular dynamics (MD) to generate candidate
Externí odkaz:
https://doaj.org/article/b80bd493f23e40339f5d153468a09724
Publikováno v:
2021 International Joint Conference on Neural Networks (IJCNN)
IJCNN
IJCNN
Deep latent generative models have attracted increasing attention due to the capacity of combining the strengths of deep learning and probabilistic models in an elegant way. The data representations learned with the models are often continuous and de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7f822eb46221c4d5b0677473178dcb4c
http://arxiv.org/abs/2304.00935
http://arxiv.org/abs/2304.00935
Recurrent neural networks are a widely used class of neural architectures. They have, however, two shortcomings. First, they are often treated as black-box models and as such it is difficult to understand what exactly they learn as well as how they a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::096cd30597b29464def93378a7a7a871
Autor:
Pierre Olivier, Mathias Niepert, Daniel Oñoro-Rubio, Hugo Lefeuvre, Alexander Jung, Felipe Huici, Charalampos Rotsos
Publikováno v:
APSys
Tuning operating systems configuration in order to obtain the maximum application performance is a hard problem. This is due to the extremely large size of the configuration space offered by modern OSes, and to the fact that it is generally explored
Autor:
Mathias Niepert, Guy Van den Broeck
Publikováno v:
An Introduction to Lifted Probabilistic Inference ISBN: 9780262365598
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5aa15715eccdb0c54ccc62b5f8fa7737
https://doi.org/10.7551/mitpress/10548.003.0015
https://doi.org/10.7551/mitpress/10548.003.0015
Autor:
Mathias Niepert, Guy Van den Broeck
Publikováno v:
An Introduction to Lifted Probabilistic Inference ISBN: 9780262365598
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::423f122dec36b4717ef0878fb519bff6
https://doi.org/10.7551/mitpress/10548.003.0017
https://doi.org/10.7551/mitpress/10548.003.0017
Publikováno v:
IJCNN
2021 International Joint Conference on Neural Networks (IJCNN)
2021 International Joint Conference on Neural Networks (IJCNN)
Graph neural networks have recently received increasing attention. These methods often map nodes into latent spaces and learn vector representations of the nodes for a variety of downstream tasks. To gain trust and to promote collaboration between AI
Autor:
Bhushan Kotnis, Kiril Gashteovski, Daniel Rubio, Ammar Shaker, Vanesa Rodriguez-Tembras, Makoto Takamoto, Mathias Niepert, Carolin Lawrence
Open Information Extraction (OpenIE) is the task of extracting (subject, predicate, object) triples from natural language sentences. Current OpenIE systems extract all triple slots independently. In contrast, we explore the hypothesis that it may be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2f8b38acc56ed83367905b5373960fa3
Representation learning for knowledge graphs (KGs) has focused on the problem of answering simple link prediction queries. In this work we address the more ambitious challenge of predicting the answers of conjunctive queries with multiple missing ent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::50b1eab08d5f0b5b730707b53d9b1208
http://arxiv.org/abs/2004.02596
http://arxiv.org/abs/2004.02596
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030454388
ECIR (1)
ECIR (1)
The text of a review expresses the sentiment a customer has towards a particular product. This is exploited in sentiment analysis where machine learning models are used to predict the review score from the text of the review. Furthermore, the product
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dad97a56f299b89ed06f4dcc55eea460
https://doi.org/10.1007/978-3-030-45439-5_16
https://doi.org/10.1007/978-3-030-45439-5_16