Results 1 to 10 of about 6,478,030 (244)
Optimizing Neural Networks with Gradient Lexicase Selection
CoRR, 2023 One potential drawback of using aggregated performance measurement in machine learning is that models may learn to accept higher errors on some training cases as compromises for lower errors on others, with the lower errors actually being instances of overfitting. This can lead to both stagnation at local optima and poor generalization.
Li Ding 0010, Lee Spector
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Neural-NGBoost: Natural gradient boosting with neural network base learners
ICT ExpressNGBoost has shown promising results in probabilistic and point estimation tasks. However, it is vague still whether this method can be scalable to neural architecture system since its base learner is based on decision trees.
Jamshidjon Ganiev +2 more
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Frontiers in Neurorobotics, 2017 To obtain the online solution of complex-valued systems of linear equation in complex domain with higher precision and higher convergence rate, a new neural network based on Zhang neural network (ZNN) is investigated in this paper. First, this new neural
Lei Ding +4 more
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Axiomatization of Gradient Smoothing in Neural Networks
CoRRGradients play a pivotal role in neural networks explanation. The inherent high dimensionality and structural complexity of neural networks result in the original gradients containing a significant amount of noise. While several approaches were proposed to reduce noise with smoothing, there is little discussion of the rationale behind smoothing ...
Linjiang Zhou +3 more
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Convergence of gradient descent for deep neural networks
CoRR, 2022 31 pages, 3 figures. Minor changes in this revision. To appear in J. Mach.
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Maintenance of delay-period activity in working memory task is modulated by local network structure.
PLoS Computational BiologyRevealing the relationship between neural network structure and function is one central theme of neuroscience. In the context of working memory (WM), anatomical data suggested that the topological structure of microcircuits within WM gradient network may
Dong Yu +7 more
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The Dynamics of Gradient Descent for Overparametrized Neural Networks
CoRR, 2021 We consider the dynamics of gradient descent (GD) in overparameterized single hidden layer neural networks with a squared loss function. Recently, it has been shown that, under some conditions, the parameter values obtained using GD achieve zero training error and generalize well if the initial conditions are chosen appropriately.
Siddhartha Satpathi, Rayadurgam Srikant
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Journal of Indian Academy of Oral Medicine and RadiologyBackground: Age estimation is the process of establishing an individual’s age using biological indicators. It is extremely important in many domains, including forensic science, anthropology, and legal medicine.
Kishorwara Ramamoorthy +3 more
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Masked Training of Neural Networks with Partial Gradients
, 2021 Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS ...
Mohtashami, Amirkeivan +2 more
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Mehran University Research Journal of Engineering and TechnologyCyber-attacks pose significant threats to the Internet and its connected resources, causing harm to institutions and governments. Advanced technologies like cloud computing, the Internet of Things (IoT), and Artificial Intelligence (AI) have made these ...
Abdul Ghani Ansari +4 more
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