6 citations · 19 across the 15 of their papers we have counts for
15 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…
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…
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…
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…
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…
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…
Ada-QPacknet -- adaptive pruning with bit width reduction as an efficient continual learning method without forgetting
Marcin Pietroń, Dominik Żurek, Kamil Faber +1
Continual Learning (CL) is a process in which there is still huge gap between human and deep learning model efficiency. Recently, many CL algorithms were designed. Most of them hav…
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…
Speedup deep learning models on GPU by taking advantage of efficient unstructured pruning and bit-width reduction
Marcin Pietroń, Dominik Żurek
This work is focused on the pruning of some convolutional neural networks (CNNs) and improving theirs efficiency on graphic processing units (GPU) by using a direct sparse algorith…
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…
When deep learning models on GPU can be accelerated by taking advantage of unstructured sparsity
Marcin Pietroń, Dominik Żurek
This paper is focused on the improvement the efficiency of the sparse convolutional neural networks (CNNs) layers on graphic processing units (GPU). The Nvidia deep neural network…
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…