no code implementations • 11 Dec 2019 • Anupriya Gogna, Angshul Majumdar, Rabab Ward
In this work we propose an autoencoder based framework for simultaneous reconstruction and classification of biomedical signals.
no code implementations • 11 Dec 2019 • Anupriya Gogna, Angshul Majumdar
Conventionally, autoencoders are unsupervised representation learning tools.
no code implementations • 11 Dec 2019 • Anupriya Gogna, Angshul Majumdar
Design of recommender systems aimed at achieving high prediction accuracy is a widely researched area.
no code implementations • 24 Dec 2015 • Anupriya Gogna, Angshul Majumdar
Our simulation results show that our method yields very accurate and robust results from only two partially sampled scans (total scan time being the same as a single echo MRI).
no code implementations • 7 May 2015 • Anupriya Gogna, Angshul Majumdar
Existing works based on latent factor models have focused on representing the rating matrix as a product of user and item latent factor matrices, both being dense.
no code implementations • 18 Dec 2013 • Anupriya Gogna, Akash Tayal
Evolutionary algorithms are metaheuristic techniques that derive inspiration from the natural process of evolution.
no code implementations • 17 Dec 2013 • Anupriya Gogna, Ankita Shukla, Angshul Majumdar
The use of Bregman technique improves the convergence speed of our algorithm and gives a higher success rate.