Results 51 to 60 of about 151,060 (281)
Neural modularity helps organisms evolve to learn new skills without forgetting old skills. [PDF]
PLoS Computational Biology, 2015 A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new ...
Kai Olav Ellefsen +2 more
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Continual Learning Objective for Analyzing Complex Knowledge Representations
Sensors, 2022 Human beings tend to incrementally learn from the rapidly changing environment without comprising or forgetting the already learned representations. Although deep learning also has the potential to mimic such human behaviors to some extent, it suffers ...
Asad Mansoor Khan +4 more
doaj +1 more source
One-Shot Neural Architecture Search: Maximising Diversity to Overcome Catastrophic Forgetting.
, 2021 One-shot neural architecture search (NAS) has recently become mainstream in the NAS community because it significantly improves computational efficiency through weight sharing.
Zhang, Miao +13 more
core +1 more source
Reducing Catastrophic Forgetting in Self-Organizing Maps [PDF]
, 2021 An agent that is capable of continual or lifelong learning is able to continuously learn from potentially infinite streams of pattern sensory data. One major historic difficulty in building agents capable of such learning is that neural systems struggle ...
Vaidya, Hitesh Ulhas Mangala
core +1 more source
Label-Guided relation prototype generation for Continual Relation Extraction [PDF]
PeerJ Computer ScienceContinual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data. To address the problem of catastrophic forgetting, some existing research endeavors have focused on exploring memory replay methods by
Shuang Liu +3 more
doaj +2 more sources
Overcoming Catastrophic Forgetting in Graph Neural Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Catastrophic forgetting refers to the tendency that a neural network ``forgets'' the previous learned knowledge upon learning new tasks. Prior methods have been focused on overcoming this problem on convolutional neural networks (CNNs), where the input samples like images lie in a grid domain, but have largely overlooked graph neural networks (GNNs ...
Huihui Liu, Yiding Yang, Xinchao Wang
openaire +3 more sources
Continual Learning for Multimodal Data Fusion of a Soft Gripper
Advanced Robotics Research, EarlyView.Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley +1 more source
Overcoming Catastrophic Forgetting by Generative Regularization
CoRR, 2019 In this paper, we propose a new method to overcome catastrophic forgetting by adding generative regularization to Bayesian inference framework. Bayesian method provides a general framework for continual learning. We could further construct a generative regularization term for all given classification models by leveraging energy-based models and ...
Patrick H. Chen +3 more
openaire +3 more sources
Addressing catastrophic forgetting for medical domain expansion
CoRR, 2021 Model brittleness is a key concern when deploying deep learning models in real-world medical settings. A model that has high performance at one institution may suffer a significant decline in performance when tested at other institutions. While pooling datasets from multiple institutions and retraining may provide a straightforward solution, it is ...
Sharut Gupta +15 more
openaire +2 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