Results 21 to 30 of about 13,735 (297)
Depth-Aware Object Tracking With a Conditional Variational Autoencoder
IEEE Access, 2021 Object tracking is a fundamental task in computer vision and artificial intelligence. However, state-of-the-art object tracking approaches are still prone to failures and are imprecise when applied to challenging scenarios, and their results are ...
Wenhui Huang, Jason Gu, Yinchen Guo
doaj +1 more source
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 +2 more sources
Assessments of Variational Autoencoder in Protein Conformation Exploration [PDF]
, 2022 Molecular dynamics (MD) simulations have been extensively used to study protein dynamics and subsequently functions. However, they are unable to sufficiently explore the conformational space within equilibrium timescales.
Sian, Xiao +3 more
core +1 more source
Penalized Variational Autoencoder for Molecular Design [PDF]
, 2019 Variational autoencoders have emerged as one of the most common approaches for automating molecular generation. We seek to learn a cross-domain latent space capturing chemical and biological information, simultaneously.
Linus, Goerlitz +3 more
core +3 more sources
Autoencoding Variational Autoencoder
CoRR, 2020 Neurips ...
A. Taylan Cemgil +4 more
openaire +2 more sources
Inverse QSAR: reversing descriptor-driven prediction pipeline using attention-based conditional variational autoencoder (ACoVAE) [PDF]
, 2022 In order to better formalize the notorious Inverse-QSAR problem (finding structures of given QSAR-predicted properties) is considered in this paper as a two-step process1,2,3 including (i) finding “seed” descriptor vectors corresponding to user ...
Gilles , Marcou +8 more
core +1 more source
Variational Bayesian Approach to Condition-Invariant Feature Extraction for Visual Place Recognition
Applied Sciences, 2021 As mobile robots perform long-term operations in large-scale environments, coping with perceptual changes becomes an important issue recently. This paper introduces a stochastic variational inference and learning architecture that can extract condition ...
Junghyun Oh, Gyuho Eoh
doaj +1 more source
Fully Spiking Variational Autoencoder [PDF]
, 2022 Spiking neural networks (SNNs) can be run on neuromorphic devices with ultra-high speed and ultra-low energy consumption because of their binary and event-driven nature.
Mukuta, Yusuke +2 more
core +1 more source
Semi-Supervised Adversarial Variational Autoencoder
Machine Learning and Knowledge Extraction, 2020 We present a method to improve the reconstruction and generation performance of a variational autoencoder (VAE) by injecting an adversarial learning. Instead of comparing the reconstructed with the original data to calculate the reconstruction loss, we ...
Ryad Zemouri
doaj +1 more source
Variational autoencoder(VAE), the learning process algorithm [82]. [PDF]
, 2023 Variational autoencoder(VAE), the learning process algorithm [82].
Zahra Rahaie (16641660) +2 more
core +1 more source