Results 41 to 50 of about 151,060 (281)
Behavioral Experiments for Understanding Catastrophic Forgetting [PDF]
, 2022 In this paper we explore whether the fundamental tool of experimental psychology, the behavioral experiment, has the power to generate insight not only into humans and animals, but artificial systems too.
Lawrence, Neil D., Bell, Samuel J.
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Habituation based synaptic plasticity and organismic learning in a quantum perovskite
Nature Communications, 2017 Habituation is a learning mechanism that enables control over forgetting and learning. Zuo, Panda et al., demonstrate adaptive synaptic plasticity in SmNiO3 perovskites to address catastrophic forgetting in a dynamic learning environment via hydrogen ...
Fan Zuo +16 more
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Multi-Scopic Cognitive Memory System for Continuous Gesture Learning
Biomimetics, 2023 With the advancement of artificial intelligence technologies in recent years, research on intelligent robots has progressed. Robots are required to understand human intentions and communicate more smoothly with humans.
Wenbang Dou +2 more
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Localizing Catastrophic Forgetting in Neural Networks
CoRR, 2019 Artificial neural networks (ANNs) suffer from catastrophic forgetting when trained on a sequence of tasks. While this phenomenon was studied in the past, there is only very limited recent research on this phenomenon. We propose a method for determining the contribution of individual parameters in an ANN to catastrophic forgetting. The method is used to
Felix Wiewel, Bin Yang 0009
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Forget Me Not: Reducing Catastrophic Forgetting for Domain Adaptation in Reading Comprehension [PDF]
2020 International Joint Conference on Neural Networks (IJCNN), 2020 The creation of large-scale open domain reading comprehension data sets in recent years has enabled the development of end-to-end neural comprehension models with promising results. To use these models for domains with limited training data, one of the most effective approach is to first pretrain them on large out-of-domain source data and then fine ...
Ying Xu +3 more
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On the role of neurogenesis in overcoming catastrophic forgetting
CoRR, 2018 Lifelong learning capabilities are crucial for artificial autonomous agents operating on real-world data, which is typically non-stationary and temporally correlated. In this work, we demonstrate that dynamically grown networks outperform static networks in incremental learning scenarios, even when bounded by the same amount of memory in both cases ...
German Ignacio Parisi +2 more
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Overcoming Catastrophic Forgetting With Unlabeled Data in the Wild [PDF]
2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019 ICCV 2019; v3 updated Figure ...
Kibok Lee 0003 +3 more
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Investigating Catastrophic Forgetting of Deep Learning Models Within Office 31 Dataset
IEEE AccessDeep learning models have shown impressive performance in various tasks. However, they are prone to a phenomenon called catastrophic forgetting. This means they do not remember what they have learned when training on new tasks. In this research paper, we
Hidayaturrahman +3 more
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Catastrophic Importance of Catastrophic Forgetting
CoRR, 2018 This paper describes some of the possibilities of artificial neural networks that open up after solving the problem of catastrophic forgetting. A simple model and reinforcement learning applications of existing methods are also proposed.
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Incremental Learning With Adaptive Model Search and a Nominal Loss Model
IEEE Access, 2022 This paper addresses an incremental learning problem, in which tasks are learned sequentially without access to the previously trained dataset. Catastrophic forgetting is a significant bottleneck to incremental learning as the network performs poorly on ...
Chanho Ahn, Eunwoo Kim, Songhwai Oh
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