Zobrazeno 1 - 10
of 83
pro vyhledávání: '"Gemulla, Rainer"'
Autor:
Kochsiek, Adrian, Gemulla, Rainer
Semi-inductive link prediction (LP) in knowledge graphs (KG) is the task of predicting facts for new, previously unseen entities based on context information. Although new entities can be integrated by retraining the model from scratch in principle,
Externí odkaz:
http://arxiv.org/abs/2310.11917
We propose KGT5-context, a simple sequence-to-sequence model for link prediction (LP) in knowledge graphs (KG). Our work expands on KGT5, a recent LP model that exploits textual features of the KG, has small model size, and is scalable. To reach good
Externí odkaz:
http://arxiv.org/abs/2305.13059
Knowledge graph embedding (KGE) models are an effective and popular approach to represent and reason with multi-relational data. Prior studies have shown that KGE models are sensitive to hyperparameter settings, however, and that suitable choices are
Externí odkaz:
http://arxiv.org/abs/2207.04979
Efficient discovery of frequent itemsets in large datasets is a key component of many data mining tasks. In-core algorithms ---which operate entirely in main memory and avoid expensive disk accesses ---and in particular the prefix tree-based algorith
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A80631
https://tud.qucosa.de/api/qucosa%3A80631/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A80631/attachment/ATT-0/
We study algorithms for efficient compression and decompression of a sequence of integers on modern hardware. Our focus is on universal codes in which the codeword length is a monotonically non-decreasing function of the uncompressed integer value; s
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A79033
https://tud.qucosa.de/api/qucosa%3A79033/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A79033/attachment/ATT-0/
This paper presents novel tree-based search algorithms that exploit the SIMD instructions found in virtually all modern processors. The algorithms are a natural extension of binary search: While binary search performs one comparison at each iteration
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A79224
https://tud.qucosa.de/api/qucosa%3A79224/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A79224/attachment/ATT-0/
Random sampling is a popular technique for providing fast approximate query answers, especially in data warehouse environments. Compared to other types of synopses, random sampling bears the advantage of retaining the dataset’s dimensionality; it a
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A82224
https://tud.qucosa.de/api/qucosa%3A82224/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A82224/attachment/ATT-0/
Autor:
Gemulla, Rainer, Lehner, Wolfgang
Random sampling is an appealing approach to build synopses of large data streams because random samples can be used for a broad spectrum of analytical tasks. Users are often interested in analyzing only the most recent fraction of the data stream in
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A80850
https://tud.qucosa.de/api/qucosa%3A80850/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A80850/attachment/ATT-0/
Random sampling is one of the most widely used means to build synopses of large datasets because random samples can be used for a wide range of analytical tasks. Unfortunately, the quality of the estimates derived from a sample is negatively affected
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A80383
https://tud.qucosa.de/api/qucosa%3A80383/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A80383/attachment/ATT-0/
Autor:
Renz-Wieland, Alexander, Kieslinger, Andreas, Gericke, Robert, Gemulla, Rainer, Kaoudi, Zoi, Markl, Volker
Parameter management is essential for distributed training of large machine learning (ML) tasks. Some ML tasks are hard to distribute because common approaches to parameter management can be highly inefficient. Advanced parameter management approache
Externí odkaz:
http://arxiv.org/abs/2206.00470