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Wyniki wyszukiwania dla: continual learning
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Divide and not forget: Ensemble of selectively trained experts in Continual Learning
PublikacjaClass-incremental learning is becoming more popular as it helps models widen their applicability while not forgetting what they already know. A trend in this area is to use a mixture-of-expert technique, where different models work together to solve the task. However, the experts are usually trained all at once using whole task data, which makes them all prone to forgetting and increasing computational burden. To address this limitation,...
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CONTINUUM MECHANICS AND THERMODYNAMICS
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Looking through the past: better knowledge retention for generative replay in continual learning
PublikacjaIn this work, we improve the generative replay in a continual learning setting to perform well on challenging scenarios. Because of the growing complexity of continual learning tasks, it is becoming more popular, to apply the generative replay technique in the feature space instead of image space. Nevertheless, such an approach does not come without limitations. In particular, we notice the degradation of the continually trained...
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Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning
PublikacjaIn this work, we investigate exemplar-free class incremental learning (CIL) with knowledge distillation (KD) as a regularization strategy, aiming to prevent forgetting. KDbased methods are successfully used in CIL, but they often struggle to regularize the model without access to exemplars of the training data from previous tasks. Our analysis reveals that this issue originates from substantial representation shifts in the teacher...
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CONTINUUM Lifelong Learning in Neurology
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Acrodermatitis continua Hallopeau
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Optics Continuum
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Hybrid Model of Axially Moving Continua
PublikacjaThe paper introduces the method of the model reduction of systems that experience a Coriolis acceleration component. It causes that system equations are non-self-adjoined. To avoid such problem modal, reduced model is built up for the system without Coriolis acceleration terms which are next included by application of any lumping technique. Hence, the final reduced model is a hybrid one, obtained by both lumping and modal methods...
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Hybrid model of moving or rotating continua
PublikacjaThe paper introduces the method of the model reduction of systems that experience a Coriolis acceleration or gyroscopic effect component. In such causes that corresponding system equations are non-self-adjoined. Modal reduced model is built up for the system without Coriolis or gyroscopic effect terms. These phenomena are next included by application of any lumping technique. Hence, the final reduced model is a hybrid one, obtained...
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MANAGING LEARNING PROCESS WITH E-LEARNING TOOL
PublikacjaThis article presents one possibility to employ Moodle, the free e-Leaning platform, to organize learning understood as a process. Behavioral approach and application to massive courses are assumed. A case study is presented, where the introduction of Moodle resulted in better student performance in homework