Results 31 to 40 of about 13,735 (297)
Graph Regularized Variational Ladder Networks for Semi-Supervised Learning
IEEE Access, 2020 To tackle the problem of semi-supervised learning (SSL), we propose a new autoencoder-based deep model. Ladder networks (LN) is an autoencoder-based method for representation learning which has been successfully applied on unsupervised learning and semi ...
Cong Hu, Xiao-Ning Song
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Nearest Neighbours Graph Variational AutoEncoder
Algorithms, 2023 Graphs are versatile structures for the representation of many real-world data. Deep Learning on graphs is currently able to solve a wide range of problems with excellent results.
Lorenzo Arsini +4 more
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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
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eVAE: Evolutionary Variational Autoencoder [PDF]
, 2023 The surrogate loss of variational autoencoders (VAEs) poses various challenges to their training, inducing the imbalance between task fitting and representation inference. To avert this, the existing strategies for VAEs focus on adjusting the tradeoff by
Wu, Zhangkai, Qi, Lei, Cao, Longbing
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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
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Tumour growth prediction of follow‐up lung cancer via conditional recurrent variational autoencoder
IET Image Processing, 2020 The prediction of lung tumour growth is the key to early treatment of lung cancer. However, the lack of intuitive and clear judgments about the future development of the tumour often leads patients to miss the best treatment opportunities.
Ning Xiao +4 more
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Multiresolution equivariant graph variational autoencoder
Machine Learning: Science and Technology, 2023 In this paper, we propose Multiresolution Equivariant Graph Variational Autoencoders (MGVAE), the first hierarchical generative model to learn and generate graphs in a multiresolution and equivariant manner. At each resolution level, MGVAE employs higher
Truong Son Hy, Risi Kondor
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Coupled Variational Autoencoder
CoRR, 2023 ICML ...
Xiaoran Hao, Patrick Shafto
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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 +2 more
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Suggestion and invention of recipes using bi-directional LSTMs-based frameworks
SN Applied Sciences, 2021 Choosing which recipe to eat and which recipe to avoid isn’t that simple for anyone. It takes strenuous efforts and a lot of time for people to calculate the number of calories and P.H level of the dish.
Sai Nikhil Rao Gona, Himamsu Marellapudi
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