Nothing will tear us apart? Uncovering grammatical features of Polish interpretese with supervised learning models

Marta Kajzer-Wietrzny; Agnieszka Chmiel; Danijel Koržinek; Przemysław Janikowski; Dariusz Jakubowski

doi:10.1080/0907676x.2025.2596717

Affiliated Institutions

Related Publications

Forward Models: Supervised Learning with a Distal Teacher

Michael I. Jordan , David E. Rumelhart

Internal models of the environment have an important role to play in adaptive systems, in general, and are of particular importance for the supervised learning paradigm. In this...

1992 Cognitive Science 1458 citations

An Overview of Overfitting and its Solutions

Ying Xue

Overfitting is a fundamental issue in supervised machine learning which prevents us from perfectly generalizing the models to well fit observed data on training data, as well as...

2019 Journal of Physics Conference Series 2055 citations

Emerging Properties in Self-Supervised Vision Transformers

Mathilde Caron , Hugo Touvron , Ishan Misra +4 more

In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets). Beyond t...

2021 2021 IEEE/CVF International Conferenc... 4220 citations

Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey

Longlong Jing , Yingli Tian

Large-scale labeled data are generally required to train deep neural networks in order to obtain better performance in visual feature learning from images or videos for computer...

2020 IEEE Transactions on Pattern Analysis... 1818 citations

When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs

Gong Cheng , Ceyuan Yang , Xiwen Yao +2 more

Remote sensing image scene classification is an active and challenging task driven by many applications. More recently, with the advances of deep learning models especially conv...

2018 IEEE Transactions on Geoscience and R... 1205 citations

Publication Info

Year: 2025
Type: article
Pages: 1-24
Citations: 0
Access: Closed

External Links

View on DOI.org

Social Impact

Altmetric

Nothing will tear us apart? Uncovering grammatical features of Polish interpretese with supervised learning models

PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

OpenAlex

Cite This

APA Style

                            
                                    Marta Kajzer-Wietrzny, 
                                
                                    Agnieszka Chmiel, 
                                
                                    Danijel Koržinek
                                
                                et al.
                            
                            (2025). 
                            Nothing will tear us apart? Uncovering grammatical features of Polish interpretese with supervised learning models. 
                            Perspectives
                            
                            , 1-24.
                            https://doi.org/10.1080/0907676x.2025.2596717

Identifiers

DOI: 10.1080/0907676x.2025.2596717