25 citations · 42 across the 6 of their papers we have counts for
6 papers
AutoNFS: Automatic Neural Feature Selection
Witold Wydmański, Marek Śmieja
Feature selection (FS) is a fundamental challenge in machine learning, particularly for high-dimensional tabular data, where interpretability and computational efficiency are criti…
VisTabNet: Adapting Vision Transformers for Tabular Data
Witold Wydmański, Ulvi Movsum-zada, Jacek Tabor +1
Although deep learning models have had great success in natural language processing and computer vision, we do not observe comparable improvements in the case of tabular data, whic…
Machine Unlearning for Recommendation Systems: An Insight
Bhavika Sachdeva, Harshita Rathee, Sristi +2
This review explores machine unlearning (MUL) in recommendation systems, addressing adaptability, personalization, privacy, and bias challenges. Unlike traditional models, MUL dyna…
HyperTab: Hypernetwork Approach for Deep Learning on Small Tabular Datasets
Witold Wydmański, Oleksii Bulenok, Marek Śmieja
Deep learning has achieved impressive performance in many domains, such as computer vision and natural language processing, but its advantage over classical shallow methods on tabu…
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann +122
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained…
Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement Learning
Witold Wydmański, Szymon Szott
The proper setting of contention window (CW) values has a significant impact on the efficiency of Wi-Fi networks. Unfortunately, the standard method used by 802.11 networks is not…