activity
20152026
most citedReal-time Dynamic MRI Reconstruction using Stacked Denoising Autoencoder

13 citations · 48 across the 53 of their papers we have counts for

collaborators
Showing eess.SPShow all

7 papers · 1 filter

eess.SP2019

Analysis Co-Sparse Coding for Energy Disaggregation

Shikha Singh, Angshul Majumdar

Energy disaggregation is the task of segregating the aggregate energy of the entire building (as logged by the smartmeter) into the energy consumed by individual appliances. This i…

eess.SP2019

Deep Sparse Coding for Non-Intrusive Load Monitoring

Shikha Singh, Angshul Majumdar

Energy disaggregation is the task of segregating the aggregate energy of the entire building (as logged by the smartmeter) into the energy consumed by individual appliances. This i…

eess.SP2019

Semi-supervised Stacked Label Consistent Autoencoder for Reconstruction and Analysis of Biomedical Signals

Anupriya Gogna, Angshul Majumdar, Rabab Ward

In this work we propose an autoencoder based framework for simultaneous reconstruction and classification of biomedical signals. Previously these two tasks, reconstruction and clas…

eess.SP20192 cited

Simultaneous Detection of Multiple Appliances from Smart-meter Measurements via Multi-Label Consistent Deep Dictionary Learning and Deep Transform Learning

Vanika Singhal, Jyoti Maggu, Angshul Majumdar

Currently there are several well-known approaches to non-intrusive appliance load monitoring rule based, stochastic finite state machines, neural networks and sparse coding. Recent…

eess.SP2019

Non-intrusive Load Monitoring via Multi-label Sparse Representation based Classification

Shikha Singh, Angshul Majumdar

This work follows the approach of multi-label classification for non-intrusive load monitoring (NILM). We modify the popular sparse representation based classification (SRC) approa…

eess.SP2019

Blind Denoising Autoencoder

Angshul Majumdar

The term blind denoising refers to the fact that the basis used for denoising is learnt from the noisy sample itself during denoising. Dictionary learning and transform learning ba…