17 citations · 27 across the 5 of their papers we have counts for
7 papers
CodNN -- Robust Neural Networks From Coded Classification
Netanel Raviv, Siddharth Jain, Pulakesh Upadhyaya +2
Deep Neural Networks (DNNs) are a revolutionary force in the ongoing information revolution, and yet their intrinsic properties remain a mystery. In particular, it is widely known…
Functional Error Correction for Robust Neural Networks
Kunping Huang, Paul Siegel, Anxiao +1
When neural networks (NeuralNets) are implemented in hardware, their weights need to be stored in memory devices. As noise accumulates in the stored weights, the NeuralNet's perfor…
Machine Learning for Error Correction with Natural Redundancy
Pulakesh Upadhyaya, Anxiao Jiang
The persistent storage of big data requires advanced error correction schemes. The classical approach is to use error correcting codes (ECCs). This work studies an alternative appr…
Representation-Oblivious Error Correction by Natural Redundancy
Pulakesh Upadhyaya, Anxiao, Jiang
Storage systems have a strong need for substantially improving their error correction capabilities, especially for long-term storage where the accumulating errors can exceed the de…
U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting
Ramakrishna Prabhu, Xiaojing Yu, Zhangyang Wang +3
This paper studies the challenging problem of fingerprint image denoising and inpainting. To tackle the challenge of suppressing complicated artifacts (blur, brightness, contrast,…
Balanced Modulation for Nonvolatile Memories
Hongchao Zhou, Anxiao, Jiang +1
This paper presents a practical writing/reading scheme in nonvolatile memories, called balanced modulation, for minimizing the asymmetric component of errors. The main idea is to e…