Search Results for author: Harinath Garudadri

Found 5 papers, 2 papers with code

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 complexity of any CPWL function is 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.

Speech Recovery for Real-World Self-powered Intermittent Devices

no code implementations9 Jun 2021 Yu-Chen Lin, Tsun-An Hsieh, Kuo-Hsuan Hung, Cheng Yu, Harinath Garudadri, Yu Tsao, Tei-Wei Kuo

The incompleteness of speech inputs severely degrades the performance of all the related speech signal processing applications.

A Unified Framework for Sparse Non-Negative Least Squares using Multiplicative Updates and the Non-Negative Matrix Factorization Problem

no code implementations7 Apr 2016 Igor Fedorov, Alican Nalci, Ritwik Giri, Bhaskar D. Rao, Truong Q. Nguyen, Harinath Garudadri

We show that the proposed framework encompasses a large class of S-NNLS algorithms and provide a computationally efficient inference procedure based on multiplicative update rules.

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