Next Generation Deep Learning

We are out to replace several decades old backpropagation with state-of-the-art optimizing paradigms, bringing in new capabilities in deep learning, such as learning unsupervised convolutional filters and marrying two of the hottest topics in machine learning and signal processing – deep learning with graph signal processing.
  • J. Maggu, A. Majumdar, E. Chouzenoux and G. Chierchia, “Deeply Transformed Subspace Clustering”, Signal Processing, Vol. 174, 107628, 2020.
  • P. Gupta, J. Maggu, A. Majumdar, E. Chouzenoux and G. Chierchia, “DeConFuse: A Deep Convolutional Transform based Unsupervised Fusion Framework”, EURASIP Journal on Advances in Signal Processing, no. 26, 2020.
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Energy Analytics

Smartgrids have bought IoT and AI to the power sector. Here we develop latest machine learning models that are tailored for this industry. The emphasis is primarily AI based demand side management with human in the loop. We concentrate on energy disaggregation, demand forecasting and anomaly detection.
  • S. Singh and A. Majumdar, “Non-intrusive load Monitoring via Multi-label Sparse Representation based Classification”, IEEE Transactions on Smart Grids, vol. 11, no. 2, pp. 1799-1801, 2020.
  • M. Gaur, S. Makonin, I. V. Bajić and A. Majumdar, "Performance Evaluation of Techniques for Identifying Abnormal Energy Consumption in Buildings," IEEE Access, vol. 7, pp. 62721-62733, 2019.
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In the past, we have worked on analysis of single cell RNA sequences. Currently our efforts are geared towards problems on computational drug discovery, e.g. drug target interactions, drug disease association and drug virus association.
  • P. Rai, D. Sengupta and A. Majumdar, “SelfE: Gene Selection via Self Expression for Single-Cell Tata”, IEEE Transactions on Computational Biology and Bioinformatics (accepted).
  • D. Talwar, A. Mongia, D. Sengupta and A. Majumdar, “AutoImpute: Autoencoder based imputation of single-cell RNA-seq data”, Nature Scientific Reports, vol. 8, no. 1, pp. 1-11, 2018.
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Remote Sensing

We develop novel machine learning algorithms for hyperspectral image classification. The emphasis is on inferencing in low-data regimes.
  • V. Singhal and A. Majumdar, “Row-Sparse Discriminative Deep Dictionary Learning for Hyperspectral Image Classification”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 11 (12), 5019 – 5028, 2019.
  • V. Singhal, H. Agrawal, S. Tariyal and A. Majumdar, “Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification”, IEEE Transactions on Geosciences and Remote Sensing, Vol. 55 (9), pp. 5274-5283, 2017.
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Inverse Problems

Solution of an under-determined system of linear equations finds many applications in signal processing. Tasks like reconstruction, denoising, deblurring, deconvolution, inverse half-toning, super-resolution, all fall under the category of inverse problems. We solve such problems by interecting the latest in signal processing and machine learning.
  • V. Singhal and A. Majumdar, “A domain adaptation approach to solve inverse problems in imaging via coupled deep dictionary learning”, Pattern Recognition, vol. 100, 2020.
  • J. Mehta and A. Majumdar, “RODEO: Robust DE-aliasing autoencOder for Real-time Medical Image Reconstruction”, Pattern Recognition, Vol. 63, pp. 499-510, 2017.
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Recommender Systems

Collaborative filtering is synonymous with recommender systems. We formulate it as a matrix completion problem and develop powerful mathematical tools that combines the former with graph signal processing.
  • A. Mongia, N. Jhamb, E. Chouzenoux and A. Majumdar, “Deep Latent Factor Model for Collaborative Filtering”, Signal Processing, Vol. 169, 107366, 2020.
  • A. Mongia and A. Majumdar, “Matrix Completion on Multiple Graphs: Application in Collaborative Filtering”, Signal Processing, Vol. 165, pp. 144-148, 2019.
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