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Found 7 papers, 0 papers with code
Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework
Design of recommender systems aimed at achieving high prediction accuracy is a widely researched area.
Semi-supervised Stacked Label Consistent Autoencoder for Reconstruction and Analysis of Biomedical Signals
In this work we propose an autoencoder based framework for simultaneous reconstruction and classification of biomedical signals.
Discriminative Autoencoder for Feature Extraction: Application to Character Recognition
Conventionally, autoencoders are unsupervised representation learning tools.
Fast Acquisition for Quantitative MRI Maps: Sparse Recovery from Non-linear Measurements
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).
Blind Compressive Sensing Framework for Collaborative Filtering
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.
Comparative analysis of evolutionary algorithms for image enhancement
Evolutionary algorithms are metaheuristic techniques that derive inspiration from the natural process of evolution.
Matrix recovery using Split Bregman
The use of Bregman technique improves the convergence speed of our algorithm and gives a higher success rate.
