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Learn to synchronize, synchronize to learn [PDF]
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2021 In recent years, the artificial intelligence community has seen a continuous interest in research aimed at investigating dynamical aspects of both training procedures and machine learning models. Of particular interest among recurrent neural networks, we have the Reservoir Computing (RC) paradigm characterized by conceptual simplicity and a fast ...
Pietro Verzelli +2 more
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Object Detection With Deep Learning: A Review [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2018 Due to object detection’s close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures.
Zhong-Qiu Zhao +3 more
semanticscholar +1 more source
, 1998 Preface. Part I: Overview Articles. 1. Learning to Learn: Introduction and Overview S. Thrun, L. Pratt. 2. A Survey of Connectionist Network Reuse Through Transfer L. Pratt, B. Jennings. 3. Transfer in Cognition A. Robins. Part II: Prediction. 4. Theoretical Models of Learning to Learn J. Baxter. 5. Multitask Learning R. Caruana. 6.
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Learning without Forgetting [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016 When building a unified vision system or gradually adding new apabilities to a system, the usual assumption is that training data for all tasks is always available.
Zhizhong Li, Derek Hoiem
semanticscholar +1 more source
Current Opinion in Behavioral Sciences, 2019 In good old-fashioned artificial intelligence (GOFAI), humans specified systems that solved problems. Much of the recent progress in AI has come from replacing human insights by learning. However, learning itself is still usually built by humans -- specifically the choice that parameter updates should follow the gradient of a cost function.
Benjamin James Lansdell +1 more
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Learning to Learn in Simulation
CoRR, 2019 AAAI-19 Workshop on Games and Simulations for Artificial ...
Ervin Teng, Bob Iannucci
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Learning Learning Curves [PDF]
Pattern Analysis and ApplicationsContains fulltext : 314824.pdf (Publisher’s version ) (Open Access)
Taylan Turan, O. +3 more
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, 2020 Continual lifelong learning requires an agent or model to learn many sequentially ordered tasks, building on previous knowledge without catastrophically forgetting it. Much work has gone towards preventing the default tendency of machine learning models to catastrophically forget, yet virtually all such work involves manually-designed solutions to the ...
Shawn Beaulieu +6 more
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Retraction Notice Whiting 2025
Journal of Learning Development in Higher EducationThe Editors of the Journal of Learning Development in Higher Education are retracting this article following notification by the author and subsequent confirmation from York St John University.
Journal of Learning Development in Higher Education
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PeFLL: Personalized Federated Learning by Learning to Learn
, 2023 We present PeFLL, a new personalized federated learning algorithm that improves over the state-of-the-art in three aspects: 1) it produces more accurate models, especially in the low-data regime, and not only for clients present during its training phase, but also for any that may emerge in the future; 2) it reduces the amount of on-client computation ...
Scott, Jonathan A +2 more
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