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pro vyhledávání: '"WHITAKER, TIM"'
Autor:
Whitaker, Tim
Neural network ensembles have been effectively used to improve generalization by combining the predictions of multiple independently trained models. However, the growing scale and complexity of deep neural networks have led to these methods becoming
Externí odkaz:
http://arxiv.org/abs/2311.14101
Autor:
Whitaker, Tim
Gradient descent methods have long been the de facto standard for training deep neural networks. Millions of training samples are fed into models with billions of parameters, which are slowly updated over hundreds of epochs. Recently, it's been shown
Externí odkaz:
http://arxiv.org/abs/2302.05984
Autor:
Whitaker, Tim, Whitley, Darrell
Neuroevolution is a promising area of research that combines evolutionary algorithms with neural networks. A popular subclass of neuroevolutionary methods, called evolution strategies, relies on dense noise perturbations to mutate networks, which can
Externí odkaz:
http://arxiv.org/abs/2302.05832
Autor:
Whitaker, Tim, Whitley, Darrell
Diversity is an important consideration in the construction of robust neural network ensembles. A collection of well trained models will generalize better if they are diverse in the patterns they respond to and the predictions they make. Diversity is
Externí odkaz:
http://arxiv.org/abs/2302.05822
Autor:
Whitaker, Tim, Whitley, Darrell
Rectified Linear Units (ReLU) are the default choice for activation functions in deep neural networks. While they demonstrate excellent empirical performance, ReLU activations can fall victim to the dead neuron problem. In these cases, the weights fe
Externí odkaz:
http://arxiv.org/abs/2302.05818
Autor:
Whitaker, Tim, Whitley, Darrell
Ensemble Learning is an effective method for improving generalization in machine learning. However, as state-of-the-art neural networks grow larger, the computational cost associated with training several independent networks becomes expensive. We in
Externí odkaz:
http://arxiv.org/abs/2202.11782
Publikováno v:
International Journal of Applied Research in Veterinary Medicine. Mar2011, Vol. 9 Issue 1, p6-14. 9p. 1 Chart, 4 Graphs.
