Improved Fast Gauss Transform
author:
Vikas Raykar,
University of Maryland
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| Slides | |
| 0:00 | The improved fast Gauss transform |
| 0:16 | Introduction |
| 0:49 | Supervised Learning |
| 1:43 | Prediction |
| 1:54 | Training |
| 2:32 | Unsupervised Learning |
| 2:57 | Gaussian kernel |
| 3:24 | Improved fast Gauss transform |
| 4:12 | Improved fast Gauss transform1 |
| 4:47 | Brief idea of the IFGT |
| 6:14 | IFGT Illustration |
| 6:56 | Sample results |
| 7:24 | IFGT can handle large dimensions |
| 8:13 | Segmentation using adaptive mean-shift |
| 8:32 | Gaussian process regression-Training times |
| 8:45 | Issues with IFGT presented in Yang et.al. 2005 |
| 10:05 | Improvements to IFGT since NIPS 2005 |
| 10:57 | Choice of parameters is crucial |
| 11:29 | IFGT expansion is both local as well as far-field |
| 12:09 | Effect of bandwidth |
| 12:52 | Some recent extensions |
| 13:01 | IFGT with variable source scales |
| 13:14 | Variable bandwidth density estimation |
| 13:18 | Variable bandwidth density estimation1 |
| 13:29 | Derivative of kernel sums |
| 14:04 | Gaussian process regression |
| 14:22 | Speedup GP regression via IFGT |
| 17:08 | How to choose |
| 18:04 | Conclusions |
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