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Joseph D. Daws Jr.
Joseph D. Daws Jr.
Machine Learning Engineer at Lirio AI Research
Verified email at lirio.com - Homepage
Title
Cited by
Cited by
Year
A polynomial-based approach for architectural design and learning with deep neural networks
J Daws Jr, CG Webster
arXiv preprint arXiv:1905.10457, 2019
102019
Analysis of deep neural networks with quasi-optimal polynomial approximation rates
J Daws, C Webster
arXiv preprint arXiv:1912.02302, 2019
92019
An adaptive stochastic gradient-free approach for high-dimensional blackbox optimization
A Dereventsov, CG Webster, J Daws
Proceedings of International Conference on Computational Intelligence: ICCI …, 2022
72022
Offline policy comparison under limited historical agent-environment interactions
A Dereventsov, JD Daws Jr, C Webster
arXiv preprint arXiv:2106.03934, 2021
22021
A Weighted -Minimization Approach For Wavelet Reconstruction of Signals and Images
J Daws Jr, A Petrosyan, H Tran, CG Webster
arXiv preprint arXiv:1909.07270, 2019
22019
Applications of nonlinear approximation for problems in learning theory and applied mathematics
JD Daws Jr
2020
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Articles 1–6