Zobrazeno 1 - 10
of 300
pro vyhledávání: '"Krawczyk, Bartosz"'
Continual learning poses a fundamental challenge for modern machine learning systems, requiring models to adapt to new tasks while retaining knowledge from previous ones. Addressing this challenge necessitates the development of efficient algorithms
Externí odkaz:
http://arxiv.org/abs/2404.04002
Autor:
Korycki, Lukasz, Krawczyk, Bartosz
Continual learning models for stationary data focus on learning and retaining concepts coming to them in a sequential manner. In the most generic class-incremental environment, we have to be ready to deal with classes coming one by one, without any h
Externí odkaz:
http://arxiv.org/abs/2307.04094
Deep learning models are being increasingly applied to imbalanced data in high stakes fields such as medicine, autonomous driving, and intelligence analysis. Imbalanced data compounds the black-box nature of deep networks because the relationships be
Externí odkaz:
http://arxiv.org/abs/2212.07743
Machine learning (ML) is playing an increasingly important role in rendering decisions that affect a broad range of groups in society. ML models inform decisions in criminal justice, the extension of credit in banking, and the hiring practices of cor
Externí odkaz:
http://arxiv.org/abs/2207.06084
Deep learning models tend to memorize training data, which hurts their ability to generalize to under-represented classes. We empirically study a convolutional neural network's internal representation of imbalanced image data and measure the generali
Externí odkaz:
http://arxiv.org/abs/2207.06080
Publikováno v:
Machine Learning, 2023
Class imbalance poses new challenges when it comes to classifying data streams. Many algorithms recently proposed in the literature tackle this problem using a variety of data-level, algorithm-level, and ensemble approaches. However, there is a lack
Externí odkaz:
http://arxiv.org/abs/2204.03719
Autor:
Korycki, Łukasz, Krawczyk, Bartosz
Mining data streams poses a number of challenges, including the continuous and non-stationary nature of data, the massive volume of information to be processed and constraints put on the computational resources. While there is a number of supervised
Externí odkaz:
http://arxiv.org/abs/2112.11019
Structural concept complexity, class overlap, and data scarcity are some of the most important factors influencing the performance of classifiers under class imbalance conditions. When these effects were uncovered in the early 2000s, understandably,
Externí odkaz:
http://arxiv.org/abs/2107.14194
Publikováno v:
ACM Comput. Surv. (June 2022)
Learning from imbalanced data is among the most challenging areas in contemporary machine learning. This becomes even more difficult when considered the context of big data that calls for dedicated architectures capable of high-performance processing
Externí odkaz:
http://arxiv.org/abs/2107.11508
Despite over two decades of progress, imbalanced data is still considered a significant challenge for contemporary machine learning models. Modern advances in deep learning have magnified the importance of the imbalanced data problem. The two main ap
Externí odkaz:
http://arxiv.org/abs/2105.02340