A Joint Semi-Supervised Variational Autoencoder and Transfer Learning Model for Designing Molecular Transition Metal Complexes [PDF]
, 2023 Deep generative models (DGMs) have shown great promise in the generation of organic molecules and inorganic materials with chemical sensible structures and optimized properties.
Tzuhsiung, Yang +3 more
core +2 more sources
Physical-Layer Authentication Based on Hierarchical Variational Autoencoder for Industrial Internet of Things [PDF]
IEEE Internet of Things Journal, 2023 Recently, physical-layer authentication (PLA) has attracted much attention since it takes advantage of the channel randomness nature of transmission media to achieve communication confidentiality and authentication.
Rui Meng +6 more
semanticscholar +1 more source
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
A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-Based Variational Autoencoder [PDF]
IEEE Robotics and Automation Letters, 2017 The detection of anomalous executions is valuable for reducing potential hazards in assistive manipulation. Multimodal sensory signals can be helpful for detecting a wide range of anomalies.
Daehyung Park, Yuuna Hoshi, C. Kemp
semanticscholar +1 more source
Diffusion probabilistic models enhance variational autoencoder for crystal structure generative modeling [PDF]
Scientific Reports, 2023 The crystal diffusion variational autoencoder (CDVAE) is a machine learning model that leverages score matching to generate realistic crystal structures that preserve crystal symmetry.
T. Pakornchote +6 more
semanticscholar +1 more source
Autoencoding Variational Autoencoder
CoRR, 2020 Neurips ...
A. Taylan Cemgil +4 more
openaire +2 more sources
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
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 +2 more sources
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
3D‐Var data assimilation using a variational autoencoder [PDF]
Quarterly Journal of the Royal Meteorological Society, 2023 Data assimilation of atmospheric observations traditionally relies on variational and Kalman filter methods. Here, an alternative neural network data assimilation (NNDA) with variational autoencoder (VAE) is proposed.
Boštjan Melinc, Ž. Zaplotnik
semanticscholar +1 more source