CNN-Transformer with Absolute Positional Encoding Optimized for Low-Dimensional Inputs: Applied to Estimate Sliding Drop Width

Shumaly et al. "CNN-Transformer with Absolute Positional Encoding Optimized for Low-Dimensional Inputs: Applied to Estimate Sliding Drop Width." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025. doi:10.1007/978-3-032-06118-8_1

[Shumaly et al. "CNN-Transformer with Absolute Positional Encoding Optimized for Low-Dimensional Inputs: Applied to Estimate Sliding Drop Width." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025.](https://mlanthology.org/ecmlpkdd/2025/shumaly2025ecmlpkdd-cnntransformer/) doi:10.1007/978-3-032-06118-8_1

@inproceedings{shumaly2025ecmlpkdd-cnntransformer,
  title     = {{CNN-Transformer with Absolute Positional Encoding Optimized for Low-Dimensional Inputs: Applied to Estimate Sliding Drop Width}},
  author    = {Shumaly, Sajjad and Darvish, Fahimeh and Salehi, Mahsa and Foumani, Navid Mohammadi and Kukharenko, Oleksandra and Butt, Hans-Jürgen and Schwanecke, Ulrich and Berger, Rüdiger},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2025},
  pages     = {3-21},
  doi       = {10.1007/978-3-032-06118-8_1},
  url       = {https://mlanthology.org/ecmlpkdd/2025/shumaly2025ecmlpkdd-cnntransformer/}
}