Results 121 to 130 of about 4,772,239 (326)

Self-Adaptive Evolutionary Info Variational Autoencoder

Computers
With the advent of increasingly powerful machine learning algorithms and the ability to rapidly obtain accurate aerodynamic performance data, there has been a steady rise in the use of algorithms for automated aerodynamic design optimisation.
Toby A. Emm, Yu Zhang
doaj   +1 more source

Generating In-Between Images Through Learned Latent Space Representation Using Variational Autoencoders

IEEE Access, 2020
Image interpolation is often implemented using one of two methods: optical flow or convolutional neural networks. These methods are typically pixel-based; they do not work well on objects between images far apart.
Paulino Cristovao, Hidemoto Nakada, Yusuke Tanimura, Hideki Asoh   +3 more
doaj   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Condition‐Associated Pattern Extraction and Recovery From Multi‐Condition Single‐Cell RNA‐seq Data With CAPER

Advanced Science, EarlyView.
Decoupling biological signals from unwanted variation in multi‑condition single‑cell RNA sequencing data remains challenging. CAPER disentangles condition‑associated biological effects from sample heterogeneity through matrix factorization, producing interpretable latent factors and a batch‑corrected expression matrix.
Ye Li, Jin Ning, An Wang, Minxi Shi, Yuanze Chen, Guoliang Liu, Shiquan Sun   +6 more
wiley   +1 more source

Tree Variational Autoencoder for Code

IEEE Access
Autoencoder models of source code are an emerging alternative to autoregressive large language models with important benefits for genetic improvement of software.
Vadim Liventsev, Sander de Bruin, Aki Harma, Milan Petkovic   +3 more
doaj   +1 more source

Ladder Variational Autoencoder [PDF]

, 2016
Variational autoencoders are powerful models for unsupervised learning. However deep models with several layers of dependent stochastic variables are difficult totrain which limits the improvements obtained using these highly expressive models.
Sønderby, Casper Kaae, Winther, Ole, Maaløe, Lars, Raiko, Tapani, Sønderby, Søren Kaae   +4 more
core  

Physics-enhanced Gaussian Process Variational Autoencoder

, 2023
Variational autoencoders allow to learn a lower-dimensional latent space based on high-dimensional input/output data. Using video clips as input data, the encoder may be used to describe the movement of an object in the video without ground truth data ...
Wu, Qirui, Pappas, George J., Beckers, Thomas   +2 more
core  

A Generative Neuro‐Symbolic AI for Protein Sequence Design

Advanced Science, EarlyView.
We introduce EffieDes, a neuro‐symbolic framework coupling deep learning‐based fitness landscape parameterization with exact automated reasoning. Unlike greedy sampling, EffieDes identifies sequences that globally optimize fitness while satisfying intricate design constraints.
Marianne Defresne, Delphine Dessaux, Samuel Buchet, Lucie Barthe, Liza Ammar‐Khodja, Bessam Azizi, Valentin Durante, Gianluca Cioci, Simon de Givry, Alain Roussel, Luis F. Garcia‐Alles, Thomas Schiex, Sophie Barbe   +12 more
wiley   +1 more source

Biadversarial Variational Autoencoder

CoRR, 2019
In the original version of the Variational Autoencoder, Kingma et al. assume Gaussian distributions for the approximate posterior during the inference and for the output during the generative process. This assumptions are good for computational reasons, e.g. we can easily optimize the parameters of a neural network using the reparametrization trick and
openaire   +2 more sources

The Variational Fair Autoencoder

, 2015
We investigate the problem of learning representations that are invariant to certain nuisance or sensitive factors of variation in the data while retaining as much of the remaining information as possible. Our model is based on a variational autoencoding architecture with priors that encourage independence between sensitive and latent factors of ...
Louizos, C., Swersky, K., Li, Y., Welling, M., Zemel, R.   +4 more
openaire   +4 more sources