Search Results for author: Kuan-Lin Chen

Found 5 papers, 2 papers with code

Leveraging Heteroscedastic Uncertainty in Learning Complex Spectral Mapping for Single-channel Speech Enhancement

no code implementations16 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.

Speech Enhancement

Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions

1 code implementation13 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.

A Generalized Proportionate-Type Normalized Subband Adaptive Filter

no code implementations17 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.

ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees

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.

Deep Learning and Control Algorithms of Direct Perception for Autonomous Driving

no code implementations26 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.

Autonomous Driving

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