This is a project about Book Recommendation using Variational Autoencoders. I tried to find the most appropriate parameters for the dataset during the model training session. A comprehensive ...

This repository provides a streamlined pipeline for implementing β-variational autoencoders (β-VAEs) on a given dataset. The β-VAE model identifies a low-dimensional latent space that represents the ...

The sequence of amino acids within a protein dictates its structure and function. Protein engineering campaigns seek to discover protein sequences with desired functions. Data-driven models of the ...

Abstract: Regularization of inverse problems is of paramount importance in computational imaging. The ability of neural networks to learn efficient image representations has been recently exploited to ...

Abstract: We present methods for semi-supervised learning (SSL) from few pilots over nonlinear channels using variational autoencoders. These channels, which are unknown at the receiver, may have ...

Variational Autoencoders (VAEs) are an artificial neural network architecture to generate new data. They are similar to regular autoencoders, which consist of an encoder and decoder. The encoder takes ...

ABSTRACT: Anomaly detection in complex crowd scenes is a challenging task due to the inherent variability in crowd behaviors, interactions, and scales. This paper proposes a novel hybrid model that ...