In the POCS-based clustering algorithm, ... To this end, an off-the-shelf FaceNet model and an autoencoder network are adopted to synthesize two sets of feature embeddings from the Five Celebrity ...

Towards a Deep Learning Autoencoder algorithm for Collaborative Filtering Recommendation Abstract: Deep learning has received leapfrog progress in the realm of Machine learning such as image ...

This project contains an Autoencoder, built and trained using Tensorflow, and used to vectorize images, so a kNN algorithm can check for image similarity. It contains two major Python notebooks, one ...

Ideally, one could leverage a large unlabeled data-set to improve generalization of a much smaller (possibly even incomplete) labeled data-set. We propose a new algorithm, Denoising Autoencoder ...

Model making. To develop their model, the researchers used a machine learning algorithm called an autoencoder, which automatically integrates gigantic swaths of data into a concise representation ...

In this paper, we propose a new algorithm called N2A-SVM (Node2vec Autoencoder-Support Vector Machine) to predict genes associated with Parkinson's disease. The contributions of our work are as ...

Using the 2D potential, we further demonstrate that our LPC algorithm outperforms the previous path-lumping algorithms by making substantially fewer incorrect assignments of individual pathways to ...