Results 31 to 40 of about 33,100 (246)
Pseudorehearsal in actor-critic agents with neural network function approximation [PDF]
, 2018 Catastrophic forgetting has a significant negative impact in reinforcement learning. The purpose of this study is to investigate how pseudorehearsal can change performance of an actor-critic agent with neural-network function approximation.
Marochko, Vladimir +3 more
core +9 more sources
CL3: Generalization of Contrastive Loss for Lifelong Learning
Journal of Imaging, 2023 Lifelong learning portrays learning gradually in nonstationary environments and emulates the process of human learning, which is efficient, robust, and able to learn new concepts incrementally from sequential experience.
Kaushik Roy +3 more
doaj +1 more source
Embodiment can combat catastrophic forgetting [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018 We use an evolutionary robotics approach to demonstrate how the choice of robot morphology can affect one specific aspect of neural networks: their ability to resist catastrophic forgetting.
Joshua P. Powers +2 more
openaire +1 more source
IEEE Access, 2023 Continual learning aims to enable neural networks to learn new tasks without catastrophic forgetting of previously learned knowledge. Orthogonal Gradient Descent algorithms have been proposed as an effective solution to mitigate catastrophic forgetting ...
Da Eun Lee +3 more
doaj +1 more source
Continual Learning With Speculative Backpropagation and Activation History
IEEE Access, 2022 Continual learning is gaining traction these days with the explosive emergence of deep learning applications. Continual learning suffers from a severe problem called catastrophic forgetting.
Sangwoo Park, Taeweon Suh
doaj +1 more source
Array heterogeneity prevents catastrophic forgetting in infants. [PDF]
Cognition, 2015 Working memory is limited in adults and infants. But unlike adults, infants whose working memory capacity is exceeded often fail in a particularly striking way: they do not represent any of the presented objects, rather than simply remembering as many objects as they can and ignoring anything further (Feigenson & Carey, 2003, 2005).
Zosh JM, Feigenson L.
europepmc +4 more sources
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
openaire +2 more sources
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
doaj +1 more source
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
doaj +1 more source
Incremental Learning of Object Detectors without Catastrophic Forgetting [PDF]
, 2017 Despite their success for object detection, convolutional neural networks are ill-equipped for incremental learning, i.e., adapting the original model trained on a set of classes to additionally detect objects of new classes, in the absence of the ...
Alahari, Karteek +2 more
core +5 more sources