6 citations · 7 across the 8 of their papers we have counts for
8 papers
Evolutionary fine tuning of quantized convolution-based deep learning models
Marcin Pietroń
Deep learning models are the most efficient models in many machine learning tasks. The main disadvantage when using them in IoT, mobile devices, independent autonomous or real-time…
A comprehensive study of LLM-based argument classification: from Llama through DeepSeek to GPT-5.2
Marcin Pietroń, Filip Gampel, Jakub Gomułka +2
Argument mining (AM) is an interdisciplinary research field focused on the automatic identification and classification of argumentative components, such as claims and premises, and…
Quality of Automatic Speech Recognition -- Polish Language case study -- from Wav2Vec to Scribe ElevenLabs
Marcin Pietroń, Szymon Piórkowski, Kamil Faber +6
This article concerns comparative studies on the Automatic Speech Recognition (ASR) model incorporated with the Large Language Model (LLM) used for medical interviews. The proposed…
A comprehensive study of LLM-based argument classification: from LLAMA through GPT-4o to Deepseek-R1
Marcin Pietroń, Rafał Olszowski, Jakub Gomułka +2
Argument mining (AM) is an interdisciplinary research field that integrates insights from logic, philosophy, linguistics, rhetoric, law, psychology, and computer science. It involv…
Pulling Back the Curtain on Deep Networks
Maciej Satkiewicz, Roberto Corizzo, Marcin Pietroń
In linear models, visualizing a weight vector naturally reveals the model's preferred input direction, but extending this intuition to deep networks via gradients or gradient ascen…
TinySubNets: An efficient and low capacity continual learning strategy
Marcin Pietroń, Kamil Faber, Dominik Żurek +1
Continual Learning (CL) is a highly relevant setting gaining traction in recent machine learning research. Among CL works, architectural and hybrid strategies are particularly effe…
Goal-Conditioned Decision Transformer for Multi-Goal Offline Reinforcement Learning
Paweł Gajewski, Dominik Żurek, Marcin Pietroń +1
Reinforcement learning (RL) in robotics faces significant hurdles regarding sample efficiency and generalization across varying goals. While Offline RL mitigates the need for costl…
Towards efficient deep autoencoders for multivariate time series anomaly detection
Marcin Pietroń, Dominik Żurek, Kamil Faber +1
Multivariate time series anomaly detection is a crucial problem in many industrial and research applications. Timely detection of anomalies allows, for instance, to prevent defects…