Overfitting and neural networks: conjugate gradient and backpropagation

By Steve Lawrence, C. Lee Giles. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000

318 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a75aa7166cc0bdc808a651843d2b57627e57cfba

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Citations in the corpus

An analysis of particle swarm optimizers (2002, methodology): “Lawrence and Giles have shown that the degree of overfitting is dependent on the type of algorithm that was used to train the network [76].”
Global Optimization Algorithms -- Theory and Application (2009, background): “The phenomenon of overfitting is best known and can often be encountered in the field of artificial neural networks or in curve fitting(50) [131, 132, 133, 134, 135].”
A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2021, background): “This framework has proven its capability in overcoming some of the main challenges of object detection, including overtraining problems [5,6] and false alarm distribution caused by the patches’ proposal stage.”
Sequential Match Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots (2016, background): “The reason might be GRU has already selected useful signals from the matching sequence and accumulated them in the ﬁnal state with its gate mechanism, especially when the sequence is not long, and there is no need to equip another attention mechanism on top of it.”