5 citations · 9 across the 11 of their papers we have counts for
12 papers
TSN-Affinity: Similarity-Driven Parameter Reuse for Continual Offline Reinforcement Learning
Dominik Żurek, Kamil Faber, Marcin Pietron +2
Continual offline reinforcement learning (CORL) aims to learn a sequence of tasks from datasets collected over time while preserving performance on previously learned tasks. This s…
Rethinking the Harmonic Loss via Non-Euclidean Distance Layers
Maxwell Miller-Golub, Collin Coil, Kamil Faber +4
Cross-entropy loss has long been the standard choice for training deep neural networks, yet it suffers from interpretability limitations, unbounded weight growth, and inefficiencie…
xLSTMAD: A Powerful xLSTM-based Method for Anomaly Detection
Kamil Faber, Marcin Pietroń, Dominik Żurek +1
The recently proposed xLSTM is a powerful model that leverages expressive multiplicative gating and residual connections, providing the temporal capacity needed for long-horizon fo…
AD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection framework
Marcin Pietroń, Dominik Żurek, Kamil Faber +1
Anomaly detection tools and methods enable key analytical capabilities in modern cyberphysical and sensor-based systems. Despite the fast-paced development in deep learning archite…
Efficient argument classification with compact language models and ChatGPT-4 refinements
Marcin Pietron, Rafał Olszowski, Jakub Gomułka
Argument mining (AM) is defined as the task of automatically identifying and extracting argumentative components (e.g. premises, claims, etc.) and detecting the existing relations…
Estimation of River Water Surface Elevation Using UAV Photogrammetry and Machine Learning
Radosław Szostak, Marcin Pietroń, Przemysław Wachniew +2
Unmanned aerial vehicle (UAV) photogrammetry allows for the creation of orthophotos and digital surface models (DSMs) of a terrain. However, DSMs of water bodies mapped with this t…
AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection
Marcin Pietron, Dominik Zurek, Kamil Faber +1
Anomaly detection tools and methods present a key capability in modern cyberphysical and failure prediction systems. Despite the fast-paced development in deep learning architectur…
From MNIST to ImageNet and Back: Benchmarking Continual Curriculum Learning
Kamil Faber, Dominik Zurek, Marcin Pietron +3
Continual learning (CL) is one of the most promising trends in recent machine learning research. Its goal is to go beyond classical assumptions in machine learning and develop mode…
Fast and scalable neuroevolution deep learning architecture search for multivariate anomaly detection
M. Pietroń, D. Żurek, K. Faber +1
Neuroevolution is one of the methodologies that can be used for learning optimal architecture during training. It uses evolutionary algorithms to generate the topology of artificia…
Ensemble neuroevolution based approach for multivariate time series anomaly detection
Kamil Faber, Dominik Żurek, Marcin Pietroń +1
Multivariate time series anomaly detection is a very common problem in the field of failure prevention. Fast prevention means lower repair costs and losses. The amount of sensors i…
Training with reduced precision of a support vector machine model for text classification
Dominik Żurek, Marcin Pietroń
This paper presents the impact of using quantization on the efficiency of multi-class text classification in the training process of a support vector machine (SVM). This work is fo…
Improving text classification with vectors of reduced precision
Krzysztof Wróbel, Maciej Wielgosz, Marcin Pietroń +2
This paper presents the analysis of the impact of a floating-point number precision reduction on the quality of text classification. The precision reduction of the vectors represen…