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Subspace distillation for continual learning
Neural Networks, 2023 An ultimate objective in continual learning is to preserve knowledge learned in preceding tasks while learning new tasks. To mitigate forgetting prior knowledge, we propose a novel knowledge distillation technique that takes into the account the manifold structure of the latent/output space of a neural network in learning novel tasks.
Mehrtash Harandi +2 more
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2016
Kernel learning is the problem of determining the best kernel (either from a dictionary of fixed kernels, or from a smooth space of kernel representations) for a given task. In this paper, we describe a new approach to kernel learning that establishes connections between the Fourier-analytic representation of kernels arising out of Bochner’s theorem ...
John Moeller +4 more
openaire +1 more source
Kernel learning is the problem of determining the best kernel (either from a dictionary of fixed kernels, or from a smooth space of kernel representations) for a given task. In this paper, we describe a new approach to kernel learning that establishes connections between the Fourier-analytic representation of kernels arising out of Bochner’s theorem ...
John Moeller +4 more
openaire +1 more source
Representations for Continuous Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2017Systems deployed in unstructured environments must be able to adapt to novel situations. This requires the ability to perform in domains that may be vastly different from training domains. My dissertation focuses on the representations used in lifelong learning and how these representations enable predictions and knowledge sharing over ...
openaire +1 more source
Continuous learning in a behavioral animation
1999To model both individual behaviors and their effects upon an eco-system appears as extremely difficult. However, our first results [5] prove that an adaptation strategy cannot be chosen without taking into account the evolution of the environment. Therefore, we create a virtual world simulating resources disappearing as soon as they are exploited too ...
Jean-Denis Fouks, L. Signac
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Online continual learning in image classification: An empirical survey
Neurocomputing, 2022Scott Sanner, Zheda Mai
exaly
Is Continual Learning Truly Learning Representations Continually?
CoRR, 2022Sungmin Cha +5 more
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CLAD: A realistic Continual Learning benchmark for Autonomous Driving
Neural Networks, 2023Kuo Yang +2 more
exaly
Continual learning with attentive recurrent neural networks for temporal data classification
Neural Networks, 2023Vincent S Tseng
exaly
Non-IID data and Continual Learning processes in Federated Learning: A long road ahead
Information Fusion, 2022Fernando E Casado +2 more
exaly