Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Stubbemann P"'
Active Learning (AL) deals with identifying the most informative samples for labeling to reduce data annotation costs for supervised learning tasks. AL research suffers from the fact that lifts from literature generalize poorly and that only a small
Externí odkaz:
http://arxiv.org/abs/2408.00426
Autor:
Klötergens, Christian, Yalavarthi, Vijaya Krishna, Stubbemann, Maximilian, Schmidt-Thieme, Lars
Irregularly sampled time series with missing values are often observed in multiple real-world applications such as healthcare, climate and astronomy. They pose a significant challenge to standard deep learn- ing models that operate only on fully obse
Externí odkaz:
http://arxiv.org/abs/2405.03582
Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training
Autor:
Burchert, Johannes, Werner, Thorben, Yalavarthi, Vijaya Krishna, de Portugal, Diego Coello, Stubbemann, Maximilian, Schmidt-Thieme, Lars
As with most other data domains, EEG data analysis relies on rich domain-specific preprocessing. Beyond such preprocessing, machine learners would hope to deal with such data as with any other time series data. For EEG classification many models have
Externí odkaz:
http://arxiv.org/abs/2404.06966
Difficulties in replication and reproducibility of empirical evidences in machine learning research have become a prominent topic in recent years. Ensuring that machine learning research results are sound and reliable requires reproducibility, which
Externí odkaz:
http://arxiv.org/abs/2403.08438
Used car pricing is a critical aspect of the automotive industry, influenced by many economic factors and market dynamics. With the recent surge in online marketplaces and increased demand for used cars, accurate pricing would benefit both buyers and
Externí odkaz:
http://arxiv.org/abs/2403.03812
Autor:
Dernedde, Tim, Thyssens, Daniela, Dittrich, Sören, Stubbemann, Maximilian, Schmidt-Thieme, Lars
Relevant combinatorial optimization problems (COPs) are often NP-hard. While they have been tackled mainly via handcrafted heuristics in the past, advances in neural networks have motivated the development of general methods to learn heuristics from
Externí odkaz:
http://arxiv.org/abs/2402.04915
Real-world datasets are often of high dimension and effected by the curse of dimensionality. This hinders their comprehensibility and interpretability. To reduce the complexity feature selection aims to identify features that are crucial to learn fro
Externí odkaz:
http://arxiv.org/abs/2304.02455
Publikováno v:
Transactions on Machine Learning Research, 2023
The concept of dimension is essential to grasp the complexity of data. A naive approach to determine the dimension of a dataset is based on the number of attributes. More sophisticated methods derive a notion of intrinsic dimension (ID) that employs
Externí odkaz:
http://arxiv.org/abs/2210.05301
Autor:
Stubbemann, Maximilian, Stumme, Gerd
The investigation of social networks is often hindered by their size as such networks often consist of at least thousands of vertices and edges. Hence, it is of major interest to derive compact structures that represent important connections of the o
Externí odkaz:
http://arxiv.org/abs/2110.13774
Autor:
Stubbemann, Maximilian, Stumme, Gerd
The automatic verification of document authorships is important in various settings. Researchers are for example judged and compared by the amount and impact of their publications and public figures are confronted by their posts on social media platf
Externí odkaz:
http://arxiv.org/abs/2109.01479