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pro vyhledávání: '"Zopf, Markus"'
Autor:
Zopf, Markus, Alesiani, Francesco
Graph neural networks (GNNs) are the predominant approach for graph-based machine learning. While neural networks have shown great performance at learning useful representations, they are often criticized for their limited high-level reasoning abilit
Externí odkaz:
http://arxiv.org/abs/2407.05816
Autor:
Zopf, Markus
How to aggregate information from multiple instances is a key question multiple instance learning. Prior neural models implement different variants of the well-known encoder-decoder strategy according to which all input features are encoded a single,
Externí odkaz:
http://arxiv.org/abs/2207.12013
Autor:
Zopf, Markus
It has been shown that a message passing neural networks (MPNNs), a popular family of neural networks for graph-structured data, are at most as expressive as the first-order Weisfeiler-Leman (1-WL) graph isomorphism test, which has motivated the deve
Externí odkaz:
http://arxiv.org/abs/2202.10156
Autor:
Zopf, Markus
The amount of information contained in heterogeneous text documents such as news articles, blogs, social media posts, scientific articles, discussion forums, and microblogging platforms is already huge and is going to increase further. It is not poss
Externí odkaz:
https://tuprints.ulb.tu-darmstadt.de/8976/1/Towards%20Context-free%20Information%20Importance%20Estimation.pdf
Publikováno v:
Proc. CoNLL 2019: 910-919
Answer selection aims at identifying the correct answer for a given question from a set of potentially correct answers. Contrary to previous works, which typically focus on the semantic similarity between a question and its answer, our hypothesis is
Externí odkaz:
http://arxiv.org/abs/1910.05315