Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Saefken, Benjamin"'
Autor:
Thielmann, Anton Frederik, Kumar, Manish, Weisser, Christoph, Reuter, Arik, Säfken, Benjamin, Samiee, Soheila
The analysis of tabular data has traditionally been dominated by gradient-boosted decision trees (GBDTs), known for their proficiency with mixed categorical and numerical features. However, recent deep learning innovations are challenging this domina
Externí odkaz:
http://arxiv.org/abs/2408.06291
Understanding how images influence the world, interpreting which effects their semantics have on various quantities and exploring the reasons behind changes in image-based predictions are highly difficult yet extremely interesting problems. By adopti
Externí odkaz:
http://arxiv.org/abs/2405.02295
Topic modelling was mostly dominated by Bayesian graphical models during the last decade. With the rise of transformers in Natural Language Processing, however, several successful models that rely on straightforward clustering approaches in transform
Externí odkaz:
http://arxiv.org/abs/2403.03737
Topic modeling seems to be almost synonymous with generating lists of top words to represent topics within large text corpora. However, deducing a topic from such list of individual terms can require substantial expertise and experience, making topic
Externí odkaz:
http://arxiv.org/abs/2403.03628
Extracting and identifying latent topics in large text corpora has gained increasing importance in Natural Language Processing (NLP). Most models, whether probabilistic models similar to Latent Dirichlet Allocation (LDA) or neural topic models, follo
Externí odkaz:
http://arxiv.org/abs/2303.17324
Deep neural networks (DNNs) have shown exceptional performances in a wide range of tasks and have become the go-to method for problems requiring high-level predictive power. There has been extensive research on how DNNs arrive at their decisions, how
Externí odkaz:
http://arxiv.org/abs/2302.09275
Deep neural networks (DNNs) have proven to be highly effective in a variety of tasks, making them the go-to method for problems requiring high-level predictive power. Despite this success, the inner workings of DNNs are often not transparent, making
Externí odkaz:
http://arxiv.org/abs/2301.11862
Few-shot methods for accurate modeling under sparse label-settings have improved significantly. However, the applications of few-shot modeling in natural language processing remain solely in the field of document classification. With recent performan
Externí odkaz:
http://arxiv.org/abs/2212.09422
Autor:
Buchmüller, Andreas, Kant, Gillian, Weisser, Christoph, Säfken, Benjamin, Kis-Katos, Krisztina, Kneib, Thomas
We present Twitmo, a package that provides a broad range of methods to collect, pre-process, analyze and visualize geo-tagged Twitter data. Twitmo enables the user to collect geo-tagged Tweets from Twitter and and provides a comprehensive and user-fr
Externí odkaz:
http://arxiv.org/abs/2207.11236
Extracting topics from large collections of unstructured text-documents has become a central task in current NLP applications and algorithms like NMF, LDA as well as their generalizations are the well-established current state of the art. However, es
Externí odkaz:
http://arxiv.org/abs/2111.10401