Reducing data dimensionality through optimizing neural network inputs

By Shufeng Tan, Michael L. Mayrovouniotis. 1995

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Ayush Kumar Tewari
2. Christian Theobalt
3. Michael Zollhöfer
4. Edgar Tretschk
5. Weiran Wang
6. Michael J. Black
7. Christoph Lassner
8. M. A. Carreira-Perpiñán

Citations in the corpus

• Distributed optimization of deeply nested systems (2012, background): “Various nonlinear versions of this approach exist where f is a spline (LeBlanc and Tibshirani, 1994), single-layer neural net (Tan and Mavrovouniotis, 1995), radial basis function net (Smola et al.”
• Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video (2020, methodology): “This is based on the auto-decoding framework used in DeepSDF and earlier works [80, 57].”
• Combined Input Training and Radial Basis Function Neural Networks based Nonlinear Principal Components Analysis Model Applied for Process Monitoring (2012, methodology): “In this field, many neuronal NLPCA approaches have been developed, (Hastie and Stuetzle, 1989), (Kramer, 1991), (Tan and Mavrovouniotis, 1995), (Dong and McAvoy, 1996), (Harkat et al.”