Results 41 to 50 of about 4,772,239 (326)

ProT-VAE: Protein Transformer Variational AutoEncoder for functional protein design

bioRxiv, 2023
The data-driven design of protein sequences with desired function is challenged by the absence of good theoretical models for the sequence-function mapping and the vast size of protein sequence space.
Emre Sevgen, Joshua Moller, Adrian Lange, John A. Parker, S. Quigley, J. Mayer, Poonam Srivastava, Sitaram Gayatri, D. Hosfield, Clayton M. Dilks, Claire Buchanan, Thomas Speltz, M. Korshunova, M. Livne, Michelle Gill, Rama Ranganathan, Anthony B Costa, Andrew L. Ferguson   +17 more
semanticscholar   +1 more source

Diffusion Variational Autoencoders [PDF]

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020
A standard Variational Autoencoder, with a Euclidean latent space, is structurally incapable of capturing topological properties of certain datasets. To remove topological obstructions, we introduce Diffusion Variational Autoencoders (DeltaVAE) with arbitrary (closed) manifolds as a latent space.
Luis A. Pérez Rey, Vlado Menkovski, Jacobus W. Portegies   +2 more
openaire   +3 more sources

Schematics for autoencoder and variational autoencoder.

, 2023
Both models are based on the encoder-decoder neural network structure to a learn latent space. A) An autoencoder is a deterministic model where z is a mapping of the input data. B) A variational autoencoder is a probabilistic model where the mapping z is
Mostafa Eltager (17102176), Tamim Abdelaal (7111226), Marcel J. T. Reinders (10589258), Mohammed Charrout (17102179), Stavros Makrodimitris (9689903), Ahmed Mahfouz (737928)   +5 more
core   +1 more source

Credit Card Fraud Detection Based on Improved Variational Autoencoder Generative Adversarial Network

IEEE Access, 2023
The rapid spread of mobile banking and e-commerce has coincided with a dramatic increase in fraudulent online payments in recent years. Although machine learning and deep learning are widely used in credit card fraud detection, the typical credit card ...
Yuanming Ding, Wei Kang, Jianxin Feng, Bo Peng, A. Yang   +4 more
semanticscholar   +1 more source

Disentangled Variational Autoencoder for Emotion Recognition in Conversations [PDF]

IEEE Transactions on Affective Computing, 2023
In Emotion Recognition in Conversations (ERC), the emotions of target utterances are closely dependent on their context. Therefore, existing works train the model to generate the response of the target utterance, which aims to recognise emotions ...
Kailai Yang, Tianlin Zhang, S. Ananiadou
semanticscholar   +1 more source

Quantum variational autoencoder [PDF]

Quantum Science and Technology, 2018
Variational autoencoders (VAEs) are powerful generative models with the salient ability to perform inference. Here, we introduce a quantum variational autoencoder (QVAE): a VAE whose latent generative process is implemented as a quantum Boltzmann machine (QBM). We show that our model can be trained end-to-end by maximizing a well-defined loss-function:
Amir Khoshaman, Walter Vinci, Brandon Denis, Evgeny Andriyash, Hossein Sadeghi, Mohammad H Amin   +5 more
openaire   +3 more sources

Mixtures of Variational Autoencoders [PDF]

2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA), 2020
In this paper, we develop a new deep mixture learning framework, aiming to learn underlying complex data structures. Each component in the mixture model is implemented using a Variational Autoencoder (VAE). VAE is a well known deep learning model which models a latent space data representation on a variational manifold.
Ye, Fei, Bors, Adrian Gheorghe
openaire   +3 more sources

Anomaly-Based Intrusion Detection From Network Flow Features Using Variational Autoencoder

IEEE Access, 2020
The rapid increase in network traffic has recently led to the importance of flow-based intrusion detection systems processing a small amount of traffic data.
Sultan Zavrak, Murat Iskefiyeli
doaj   +1 more source

Graph embedding and Gaussian mixture variational autoencoder network for end-to-end analysis of single-cell RNA sequencing data

Cell Reports Methods, 2023
Summary Single-cell RNA sequencing (scRNA-seq) is a revolutionary technology to determine the precise gene expression of individual cells and identify cell heterogeneity and subpopulations.
Jun-Lin Xu, Jie-Lin Xu, Yajie Meng, Changcheng Lu, Lijun Cai, Xiangxiang Zeng, R. Nussinov, F. Cheng   +7 more
semanticscholar   +1 more source

Clockwork Variational Autoencoders

CoRR, 2021
Deep learning has enabled algorithms to generate realistic images. However, accurately predicting long video sequences requires understanding long-term dependencies and remains an open challenge. While existing video prediction models succeed at generating sharp images, they tend to fail at accurately predicting far into the future.
Vaibhav Saxena, Jimmy Ba, Danijar Hafner
openaire   +3 more sources