Results 61 to 70 of about 24,131 (257)
CoRR, 2018 Most artificial intelligence models have limiting ability to solve new tasks faster, without forgetting previously acquired knowledge. The recently emerging paradigm of continual learning aims to solve this issue, in which the model learns various tasks in a sequential fashion.
Ju Xu, Zhanxing Zhu
openaire +3 more sources
SSRN Electronic Journal, 2022 70 pages, 4 figures, appended with an ...
Yanwei Jia, Xun Yu Zhou
openaire +4 more sources
Advanced Robotics Research, EarlyView.This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao +6 more
wiley +1 more source
Variance-Covariance Regularization Improves Continual Learning
IEEE AccessIn this work, we explore the benefits of Variance-Covariance Regularization in Continual Learning (CL). Neural networks suffer from abrupt performance loss when updated with additional data.
Piotr Hondra, Daniel Marczak, Kamil Deja
doaj +1 more source
Learning‐Based Soft Robotic Grasping: Recent Progress and Remaining Challenges
Advanced Robotics Research, EarlyView.This review analyzes learning‐based soft robotic grasping from a pipeline‐oriented perspective, encompassing soft gripper design, multimodal sensing, and learning‐based planning and control. It surveys key neural network architectures and benchmark datasets and identifies critical challenges such as sim‐to‐real transfer, generalization, and continual ...
Arnab Majumder +3 more
wiley +1 more source
Advanced Science, EarlyView.Liu et al. define a systems‐level interactome of fibroadipogenic progenitor (FAP)‐mediated signaling in skeletal muscle by integrating single‐cell transcriptomics with FAP depletion‐based perturbation analysis. Functional interrogation using a conditioned media bioassay links predicted signaling to multicellular outcomes, establishing a framework to ...
Xingyu Liu +13 more
wiley +1 more source
Enhancing Generative Class Incremental Learning Performance With a Model Forgetting Approach
IEEE Open Journal of Signal ProcessingThis study presents a novel approach to Generative Class Incremental Learning (GCIL) by introducing the forgetting mechanism, aimed at dynamically managing class information for better adaptation to streaming data.
Taro Togo +4 more
doaj +1 more source
Distributed Continual Learning
CoRRThis work studies the intersection of continual and federated learning, in which independent agents face unique tasks in their environments and incrementally develop and share knowledge. We introduce a mathematical framework capturing the essential aspects of distributed continual learning, including agent model and statistical heterogeneity, continual
Long Le, Marcel Hussing, Eric Eaton
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Advances and Perspectives in Graphene‐Based Quantum Dots Enabled Neuromorphic Devices
Advanced Science, EarlyView.Graphene‐based QDs are zero‐dimensional carbon nanomaterials with pronounced quantum confinement and tunable electronic structures. Herein, we summarize their synthesis strategies and functionalization methods, and highlight their functional roles and operating mechanisms in devices, as well as recent advances in neuromorphic electronics. We anticipate
Yulin Zhen +9 more
wiley +1 more source
Continual Learning With Quasi-Newton Methods
IEEE AccessCatastrophic forgetting remains a major challenge when neural networks learn tasks sequentially. Elastic Weight Consolidation (EWC) attempts to address this problem by introducing a Bayesian-inspired regularization loss to preserve knowledge of ...
Steven Vander Eeckt, Hugo Van Hamme
doaj +1 more source