1 code implementation • 20 Feb 2023 • Kuan-Lin Chen, Ching-Hua Lee, Bhaskar D. Rao, Harinath Garudadri
However, the best-performing design of T-F weights is criterion-dependent in general.
no code implementations • 16 Nov 2022 • Kuan-Lin Chen, Daniel D. E. Wong, Ke Tan, Buye Xu, Anurag Kumar, Vamsi Krishna Ithapu
During training, our approach augments a model learning complex spectral mapping with a temporary submodel to predict the covariance of the enhancement error at each time-frequency bin.
1 code implementation • 13 Oct 2022 • Kuan-Lin Chen, Harinath Garudadri, Bhaskar D. Rao
When the number of pieces is unknown, we prove that, in terms of the number of distinct linear components, the neural complexities of any CPWL function are at most polynomial growth for low-dimensional inputs and factorial growth for the worst-case scenario, which are significantly better than existing results in the literature.
no code implementations • 17 Nov 2021 • Kuan-Lin Chen, Ching-Hua Lee, Bhaskar D. Rao, Harinath Garudadri
Specifically, we study the effects of using different numbers of subbands and various sparsity penalty terms for quasi-sparse, sparse, and dispersive systems.
1 code implementation • NeurIPS 2021 • Kuan-Lin Chen, Ching-Hua Lee, Harinath Garudadri, Bhaskar D. Rao
To codify such a difference in nonlinearities and reveal a linear estimation property, we define ResNEsts, i. e., Residual Nonlinear Estimators, by simply dropping nonlinearities at the last residual representation from standard ResNets.
no code implementations • 26 Oct 2019 • Der-Hau Lee, Kuan-Lin Chen, Kuan-Han Liou, Chang-Lun Liu, Jinn-Liang Liu
Based on the direct perception paradigm of autonomous driving, we investigate and modify the CNNs (convolutional neural networks) AlexNet and GoogLeNet that map an input image to few perception indicators (heading angle, distances to preceding cars, and distance to road centerline) for estimating driving affordances in highway traffic.