Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data | Paper Citation Record

Observation 8d42f312-4f62-4cff-b9fa-1efccb0b1911 · outbound

This paper cites By combining neural networks and Bayesian inference, VAE-LF is able to effectively learn the nonlinear latent features of the data.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data By combining neural networks and Bayesian inference, VAE-LF is able to effectively learn the nonlinear latent features of the data

Reference 1

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This paper cites This app roach makes full use of the serialization processing capability of VAE and adapts to the temporal characteristics and sparsity of PLM data.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data This app roach makes full use of the serialization processing capability of VAE and adapts to the temporal characteristics and sparsity of PLM data

Reference 2

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This paper cites VAE-LF comprises two co mponents: an Encoder and a Decoder.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data VAE-LF comprises two co mponents: an Encoder and a Decoder

Reference 3

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This paper cites Fo r each parameter, it is sampled M times per day for a total of N days, which results in a time-days matrix of dimension |N|×|M|.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Fo r each parameter, it is sampled M times per day for a total of N days, which results in a time-days matrix of dimension |N|×|M|

Reference 4

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This paper cites Temporal pattern-aware QoS prediction by Biased Non-negative Tucker Factorization of tensors,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Temporal pattern-aware QoS prediction by Biased Non-negative Tucker Factorization of tensors,

Reference 5

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This paper cites Intelligent Systems for Power Load Forecasting: A Study Review,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Intelligent Systems for Power Load Forecasting: A Study Review,

Reference 6

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This paper cites An L1-and-L2-regularized nonnegative tensor factorization for power load monitoring data imputation,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data An L1-and-L2-regularized nonnegative tensor factorization for power load monitoring data imputation,

Reference 7

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This paper cites Application of load monitoring in appliances’ energy management – A review,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Application of load monitoring in appliances’ energy management – A review,

Reference 8

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Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Unresolved cited work

Reference 9

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This paper cites Modularity Maximization-Incorporated Nonnegative Tensor RESCAL Decomposition for Dynamic Community Detection,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Modularity Maximization-Incorporated Nonnegative Tensor RESCAL Decomposition for Dynamic Community Detection,

Reference 10

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This paper cites A Fast and Inherently Nonnegative Latent Factorization of Tensors Model for Dynamic Directed Network Representation,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data A Fast and Inherently Nonnegative Latent Factorization of Tensors Model for Dynamic Directed Network Representation,

Reference 11

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This paper cites A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis,

Reference 12

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This paper cites Dynamically Weighted Directed Network Link Prediction Using Tensor Ring Decomposition,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Dynamically Weighted Directed Network Link Prediction Using Tensor Ring Decomposition,

Reference 13

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This paper cites A Fine-Grained Regularization Scheme for Non-negative Latent Factorization of High- Dimensional and Incomplete Tensors,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data A Fine-Grained Regularization Scheme for Non-negative Latent Factorization of High- Dimensional and Incomplete Tensors,

Reference 14

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This paper cites Adaptively-Accelerated Parallel Stochastic Gradient Descent for High-Dimensional and Incomplete Data Representation Learning,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Adaptively-Accelerated Parallel Stochastic Gradient Descent for High-Dimensional and Incomplete Data Representation Learning,

Reference 15

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This paper cites Instance-Frequency-Weighted Regularized, Nonnegative and Adaptive Latent Factorization of Tensors for Dynamic QoS Analysis,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Instance-Frequency-Weighted Regularized, Nonnegative and Adaptive Latent Factorization of Tensors for Dynamic QoS Analysis,

Reference 16

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This paper cites Adjusting Learning Depth in Nonnegative Latent Factorization of Tensors for Accurately Modeling Temporal Patterns in Dynamic QoS Data,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Adjusting Learning Depth in Nonnegative Latent Factorization of Tensors for Accurately Modeling Temporal Patterns in Dynamic QoS Data,

Reference 17

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This paper cites Advancing Non-Negative Latent Factorization of Tensors With Diversified Regularization Schemes,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Advancing Non-Negative Latent Factorization of Tensors With Diversified Regularization Schemes,

Reference 18

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This paper cites Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors,

Reference 19

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This paper cites Asynchronous Parallel Fuzzy Stochastic Gradient Descent for High-Dimensional Incomplete Data Representation,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Asynchronous Parallel Fuzzy Stochastic Gradient Descent for High-Dimensional Incomplete Data Representation,

Reference 20

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This paper cites Latent-Factorization-of-Tensors-Incorporated Battery Cycle Life Prediction,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Latent-Factorization-of-Tensors-Incorporated Battery Cycle Life Prediction,

Reference 21

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This paper cites NeuLFT: A Novel Approach to Nonlinear Canonical Polyadic Decomposition on High- Dimensional Incomplete Tensors,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data NeuLFT: A Novel Approach to Nonlinear Canonical Polyadic Decomposition on High- Dimensional Incomplete Tensors,

Reference 22

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This paper cites An Adaptively Bias-Extended Non-Negative Latent Factorization of Tensors Model for Accurately Representing the Dynamic QoS Data,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data An Adaptively Bias-Extended Non-Negative Latent Factorization of Tensors Model for Accurately Representing the Dynamic QoS Data,

Reference 23

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This paper cites Non-Negativity Constrained Missing Data Estimation for High- Dimensional and Sparse Matrices from Industrial Applications,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Non-Negativity Constrained Missing Data Estimation for High- Dimensional and Sparse Matrices from Industrial Applications,

Reference 24

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This paper cites Improved Symmetric and Nonnegative Matrix Factorization Models for Undirected, Sparse and Large-Scaled Networks: A Triple Factorization-Based Approach,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Improved Symmetric and Nonnegative Matrix Factorization Models for Undirected, Sparse and Large-Scaled Networks: A Triple Factorization-Based Approach,

Reference 25

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This paper cites An Effective Scheme for QoS Estimation via Alternating Direction Method-Based Matrix Factorization,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data An Effective Scheme for QoS Estimation via Alternating Direction Method-Based Matrix Factorization,

Reference 26

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This paper cites A High-Order Proximity-Incorporated Nonnegative Matrix Factorization-Based Community Detector,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data A High-Order Proximity-Incorporated Nonnegative Matrix Factorization-Based Community Detector,

Reference 27

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This paper cites Highly-Accurate Community Detection via Pointwise Mutual Information-Incorporated Symmetric Non-Negative Matrix Factorization,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Highly-Accurate Community Detection via Pointwise Mutual Information-Incorporated Symmetric Non-Negative Matrix Factorization,

Reference 28

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Observation a2bf6e9e-2e93-4e83-8902-1d6a44d8fbc8 · outbound

This paper cites Symmetry and Graph Bi-Regularized Non-Negative Matrix Factorization for Precise Community Detection,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Symmetry and Graph Bi-Regularized Non-Negative Matrix Factorization for Precise Community Detection,

Reference 29

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This paper cites An Alternating-Direction-Method of Multipliers-Incorporated Approach to Symmetric Non-Negative Latent Factor Analysis,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data An Alternating-Direction-Method of Multipliers-Incorporated Approach to Symmetric Non-Negative Latent Factor Analysis,

Reference 30

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This paper cites Symmetric Nonnegative Matrix Factorization-Based Community Detection Models and Their Convergence Analysis,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Symmetric Nonnegative Matrix Factorization-Based Community Detection Models and Their Convergence Analysis,

Reference 31

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This paper cites Symmetry and Nonnegativity-Constrained Matrix Factorization for Community Detection,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Symmetry and Nonnegativity-Constrained Matrix Factorization for Community Detection,

Reference 32

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This paper cites Algorithms of Unconstrained Non-Negative Latent Factor Analysis for Recommender Systems,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Algorithms of Unconstrained Non-Negative Latent Factor Analysis for Recommender Systems,

Reference 33

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This paper cites Alternating-Direction-Method of Multipliers-Based Adaptive Nonnegative Latent Factor Analysis,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Alternating-Direction-Method of Multipliers-Based Adaptive Nonnegative Latent Factor Analysis,

Reference 34

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Observation f5616e1e-ba25-4378-bc49-8563307488e3 · outbound

This paper cites Fast and Accurate Non-Negative Latent Factor Analysis of High-Dimensional and Sparse Matrices in Recommender Systems,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Fast and Accurate Non-Negative Latent Factor Analysis of High-Dimensional and Sparse Matrices in Recommender Systems,

Reference 35

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Observation 0bdbf96c-bacc-4533-a16b-e3f930f2e687 · outbound

This paper cites Learning Error Refinement in Stochastic Gradient Descent-Based Latent Factor Analysis via Diversified PID Controllers,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Learning Error Refinement in Stochastic Gradient Descent-Based Latent Factor Analysis via Diversified PID Controllers,

Reference 36

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Observation 74cba86c-301d-40f2-9873-c3b96b77e794 · outbound

This paper cites An Instance-Frequency-Weighted Regularization Scheme for Non-Negative Latent Factor Analysis on High-Dimensional and Sparse Data,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data An Instance-Frequency-Weighted Regularization Scheme for Non-Negative Latent Factor Analysis on High-Dimensional and Sparse Data,

Reference 37

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Observation ecf8c91c-3e99-4460-8df3-f0ea89c887b6 · outbound

This paper cites Assimilating Second-Order Information for Building Non-Negative Latent Factor Analysis-Based Recommenders,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Assimilating Second-Order Information for Building Non-Negative Latent Factor Analysis-Based Recommenders,

Reference 38

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Observation aca9dfc7-0ae9-4311-bf35-ec7c9a90e4ae · outbound

This paper cites A Deep Latent Factor Model for High-Dimensional and Sparse Matrices in Recommender Systems,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data A Deep Latent Factor Model for High-Dimensional and Sparse Matrices in Recommender Systems,

Reference 39

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Observation f2577ed1-7a83-4f03-a7ad-f498ad43b33b · outbound

This paper cites A Double-Space and Double-Norm Ensembled Latent Factor Model for Highly Accurate Web Service QoS Prediction,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data A Double-Space and Double-Norm Ensembled Latent Factor Model for Highly Accurate Web Service QoS Prediction,

Reference 40

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Observation f6594530-e590-4f43-ad15-9e9e2ea98227 · outbound

This paper cites Large-scale and Scalable Latent Factor Analysis via Distributed Alternative Stochastic Gradient Descent for Recommender Systems,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Large-scale and Scalable Latent Factor Analysis via Distributed Alternative Stochastic Gradient Descent for Recommender Systems,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.567373Z

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Observation 864f6efb-134d-43e8-a4de-4be1a0c4b23e · outbound

This paper cites Generalized Nesterov’s Acceleration-Incorporated, Non-Negative and Adaptive Latent Factor Analysis,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Generalized Nesterov’s Acceleration-Incorporated, Non-Negative and Adaptive Latent Factor Analysis,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.552583Z

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Observation 404be9bf-421a-4938-ad0c-9acfc81e6bf9 · outbound

This paper cites Hierarchical Particle Swarm Optimization-incorporated Latent Factor Analysis for Large-Scale Incomplete Matrices,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Hierarchical Particle Swarm Optimization-incorporated Latent Factor Analysis for Large-Scale Incomplete Matrices,

Reference 43

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This paper cites Latent Factor Analysis Model With Temporal Regularized Constraint for Road Traffic Data Imputation,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Latent Factor Analysis Model With Temporal Regularized Constraint for Road Traffic Data Imputation,

Reference 44

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Observation 7474bc13-828f-40c1-898e-a3b34374eb4d · outbound

This paper cites Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data,

Reference 45

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 947316f4-86aa-4ca9-b216-217290465f58 · outbound

This paper cites Nonnegative Latent Factor Analysis-Incorporated and Feature-Weighted Fuzzy Double $c$-Means Clustering for Incomplete Data,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Nonnegative Latent Factor Analysis-Incorporated and Feature-Weighted Fuzzy Double $c$-Means Clustering for Incomplete Data,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.489654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f31e4534-9691-4d08-9256-aad909cfcc2a · outbound

This paper cites Position-Transitional Particle Swarm Optimization-Incorporated Latent Factor Analysis,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Position-Transitional Particle Swarm Optimization-Incorporated Latent Factor Analysis,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.470461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dacc190d-d5c5-4fb5-b5bf-04ff6e5463bc · outbound

This paper cites Proximal Alternating-Direction-Method-of-Multipliers-Incorporated Nonnegative Latent Factor Analysis,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Proximal Alternating-Direction-Method-of-Multipliers-Incorporated Nonnegative Latent Factor Analysis,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.454810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d585ca0a-006a-4f98-b9c7-05c66bb12036 · outbound

This paper cites Pseudo Gradient-Adjusted Particle Swarm Optimization for Accurate Adaptive Latent Factor Analysis,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Pseudo Gradient-Adjusted Particle Swarm Optimization for Accurate Adaptive Latent Factor Analysis,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.439652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1cb6b664-ebd3-420f-a63f-627cf2a69202 · outbound

This paper cites A Multilayered-and-Randomized Latent Factor Model for High-Dimensional and Sparse Matrices,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data A Multilayered-and-Randomized Latent Factor Model for High-Dimensional and Sparse Matrices,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.336895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 800aa8ad-955c-4825-b9a1-f7449a0a6b79 · outbound

This paper cites Parallel Adaptive Stochastic Gradient Descent Algorithms for Latent Factor Analysis of High-Dimensional and Incomplete Industrial Data,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Parallel Adaptive Stochastic Gradient Descent Algorithms for Latent Factor Analysis of High-Dimensional and Incomplete Industrial Data,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.407644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3d5b9bfc-3205-4ad6-8908-0f7e46d8ff08 · outbound

This paper cites A Data-Characteristic-Aware Latent Factor Model for Web Services QoS Prediction,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data A Data-Characteristic-Aware Latent Factor Model for Web Services QoS Prediction,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.392773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2808eaa6-d644-4a8e-8a5f-4ae9cf1ccbfe · outbound

This paper cites A Fast Non-Negative Latent Factor Model Based on Generalized Momentum Method,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data A Fast Non-Negative Latent Factor Model Based on Generalized Momentum Method,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.377488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 451fdbd2-2d47-4a0c-bd0d-3904046b4d47 · outbound

This paper cites A Generalized Nesterov-Accelerated Second-Order Latent Factor Model for High- Dimensional and Incomplete Data,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data A Generalized Nesterov-Accelerated Second-Order Latent Factor Model for High- Dimensional and Incomplete Data,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.353138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9e15d395-78b7-4423-ae2e-b38cd2d718f0 · outbound

This paper cites An Adaptive Divergence-Based Non-Negative Latent Factor Model,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data An Adaptive Divergence-Based Non-Negative Latent Factor Model,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.248186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 38bd6227-5dd9-4c95-9326-c89c472cc758 · outbound

This paper cites A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.318909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9fa79b99-038c-49f3-b6f3-cce35abd43d8 · outbound

This paper cites A Posterior-Neighborhood-Regularized Latent Factor Model for Highly Accurate Web Service QoS Prediction,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data A Posterior-Neighborhood-Regularized Latent Factor Model for Highly Accurate Web Service QoS Prediction,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.302788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 45c70769-10aa-4a3e-b259-d450316744c8 · outbound

This paper cites A Second-Order Symmetric Non-Negative Latent Factor Model for Undirected Weighted Network Representation,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data A Second-Order Symmetric Non-Negative Latent Factor Model for Undirected Weighted Network Representation,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.284303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2e5ad53c-3b18-473e-b4f8-1486a32f7411 · outbound

This paper cites A Prediction-Sampling-Based Multilayer-Structured Latent Factor Model for Accurate Representation to High-Dimensional and Sparse Data,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data A Prediction-Sampling-Based Multilayer-Structured Latent Factor Model for Accurate Representation to High-Dimensional and Sparse Data,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.266905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ca081888-ee5c-435c-aac9-e5cbbfb35e4e · outbound

This paper cites Momentum-Accelerated and Biased Unconstrained Non-Negative Latent Factor Model for Handling High-Dimensional and Incomplete Data,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Momentum-Accelerated and Biased Unconstrained Non-Negative Latent Factor Model for Handling High-Dimensional and Incomplete Data,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.170430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 38769a54-de33-498a-8a25-7060eccc36ed · outbound

This paper cites An Inherently Nonnegative Latent Factor Model for High-Dimensional and Sparse Matrices from Industrial Applications,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data An Inherently Nonnegative Latent Factor Model for High-Dimensional and Sparse Matrices from Industrial Applications,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.232689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f25f234a-8abe-4dfc-b9de-b39f85e1aa61 · outbound

This paper cites An L1 -and- L2 -Norm-Oriented Latent Factor Model for Recommender Systems,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data An L1 -and- L2 -Norm-Oriented Latent Factor Model for Recommender Systems,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.216767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 31ec77e1-ace0-4715-8a1d-43957e5a75d6 · outbound

This paper cites Convergence Analysis of Single Latent Factor-Dependent, Nonnegative, and Multiplicative Update-Based Nonnegative Latent Factor Models,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Convergence Analysis of Single Latent Factor-Dependent, Nonnegative, and Multiplicative Update-Based Nonnegative Latent Factor Models,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.201273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f8ca8bff-bb93-4c12-a1f3-9ae17ce9143a · outbound

This paper cites Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.185737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 32658141-ec6b-4444-a6f5-ac392231d020 · outbound

This paper cites Randomized latent factor model for high-dimensional and sparse matrices from industrial applications,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Randomized latent factor model for high-dimensional and sparse matrices from industrial applications,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.081090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f61a7b8e-0fbe-4143-9c65-b6b8320b4dda · outbound

This paper cites Momentum-Incorporated Symmetric Non-Negative Latent Factor Models,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Momentum-Incorporated Symmetric Non-Negative Latent Factor Models,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.152950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 077ad53b-6a5d-4f5b-b47b-a0cccbf34fdc · outbound

This paper cites Incorporation of Efficient Second-Order Solvers Into Latent Factor Models for Accurate Prediction of Missing QoS Data,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Incorporation of Efficient Second-Order Solvers Into Latent Factor Models for Accurate Prediction of Missing QoS Data,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.134692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 87c37bc3-20e9-4813-afe6-d052882da725 · outbound

This paper cites Non-Negative Latent Factor Model Based on β-Divergence for Recommender Systems,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Non-Negative Latent Factor Model Based on β-Divergence for Recommender Systems,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.116820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e35a3482-3891-409e-8b0e-85038f42388a · outbound

This paper cites Robust Low-Rank Latent Feature Analysis for Spatiotemporal Signal Recovery,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Robust Low-Rank Latent Feature Analysis for Spatiotemporal Signal Recovery,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.100104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 20fc4fbf-c783-42a6-85c6-41c0eb2f6ae7 · outbound

This paper cites A Fast Deep AutoEncoder for high-dimensional and sparse matrices in recommender systems,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data A Fast Deep AutoEncoder for high-dimensional and sparse matrices in recommender systems,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:37.779092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a56b0363-8087-486a-bbe3-2f19e636e449 · outbound

This paper cites Symmetric and Nonnegative Latent Factor Models for Undirected, High- Dimensional, and Sparse Networks in Industrial Applications,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Symmetric and Nonnegative Latent Factor Models for Undirected, High- Dimensional, and Sparse Networks in Industrial Applications,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.065458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2a8334d9-3e2a-48d1-abd7-a12826746db9 · outbound

This paper cites MMLF: Multi-Metric Latent Feature Analysis for High-Dimensional and Incomplete Data,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data MMLF: Multi-Metric Latent Feature Analysis for High-Dimensional and Incomplete Data,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.048379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 35c7ab5e-ae00-450c-ae16-e38bcd6692ad · outbound

This paper cites A Fast Nonnegative Autoencoder-Based Approach to Latent Feature Analysis on High- Dimensional and Incomplete Data,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data A Fast Nonnegative Autoencoder-Based Approach to Latent Feature Analysis on High- Dimensional and Incomplete Data,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.033149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e077fafb-ec6b-45ee-a961-26cd627f3f6b · outbound

This paper cites Autoencoder-Embedded Iterated Local Search for Energy-Minimized Task Schedules of Human–Cyber–Physical Systems,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Autoencoder-Embedded Iterated Local Search for Energy-Minimized Task Schedules of Human–Cyber–Physical Systems,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:38.017032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7bae759d-c622-4b12-9d40-a53c1b517146 · outbound

This paper cites Two-Stream Graph Convolutional Network-Incorporated Latent Feature Analysis,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Two-Stream Graph Convolutional Network-Incorporated Latent Feature Analysis,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:36.615527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e9611aa3-56cc-4c8e-bfe9-bd284a05ec63 · outbound

This paper cites An Outlier-Resilient Autoencoder for Representing High-Dimensional and Incomplete Data,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data An Outlier-Resilient Autoencoder for Representing High-Dimensional and Incomplete Data,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:37.492373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation aeba1e64-61b0-447e-9624-91b8be58de80 · outbound

This paper cites Neural Collaborative Filtering,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Neural Collaborative Filtering,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:37.336573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f8675a51-397e-4bfb-bccb-d2823f41a752 · outbound

This paper cites Predicting Protein-Protein Interactions Using Sequence and Network Information via Variational Graph Autoencoder,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Predicting Protein-Protein Interactions Using Sequence and Network Information via Variational Graph Autoencoder,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:37.074243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 363d8915-9dbf-4682-9e7d-e87646ab5a7f · outbound

This paper cites SDGNN: Symmetry-Preserving Dual-Stream Graph Neural Networks,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data SDGNN: Symmetry-Preserving Dual-Stream Graph Neural Networks,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:36.855734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5f7f0cab-a91c-4a02-ac5b-a4a9fe29bf01 · outbound

This paper cites LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:35.680979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9d57a072-d230-4daa-92d5-5f4dca6b3130 · outbound

This paper cites GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:36.325864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 88b4e00c-ba17-4ae7-b304-a0a2c4fb4146 · outbound

This paper cites A Two-Stream Light Graph Convolution Network-based Latent Factor Model for Accurate Cloud Service QoS Estimation,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data A Two-Stream Light Graph Convolution Network-based Latent Factor Model for Accurate Cloud Service QoS Estimation,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:36.144898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e52e5eba-b5c0-41ed-a07e-9e02095ae58e · outbound

This paper cites A Node-Collaboration-Informed Graph Convolutional Network for Highly Accurate Representation to Undirected Weighted Graph,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data A Node-Collaboration-Informed Graph Convolutional Network for Highly Accurate Representation to Undirected Weighted Graph,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:35.996716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cf0e9eda-7c4e-4789-b34a-b9bd757623f8 · outbound

This paper cites Graph Linear Convolution Pooling for Learning in Incomplete High-Dimensional Data,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Graph Linear Convolution Pooling for Learning in Incomplete High-Dimensional Data,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:35.862012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 66d2f80a-ecfb-4ff7-b2f2-b19dd8658730 · outbound

This paper cites Aut o-Encoding Variational Bayes,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Aut o-Encoding Variational Bayes,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:35.554880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation deb2fcc3-7b49-4df9-a4a1-aa4b71f9d078 · outbound

This paper cites Linear, or Non -Linear, That is the Question!,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Linear, or Non -Linear, That is the Question!,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:35.374733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d8e15b39-a25d-4990-acdf-000e735ceefa · outbound

This paper cites Graph Trend Filtering Networks for Recommendation,.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data Graph Trend Filtering Networks for Recommendation,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:07:35.187420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6fcf766f-59be-45c3-93ef-5aa8d09621b5 · outbound

This paper cites We use VAE to comp lement PL M missing data by firs t spl itting the PLM data into vec tors, and then inputting t he vectors sequentially to VAE for imputation.

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data We use VAE to comp lement PL M missing data by firs t spl itting the PLM data into vec tors, and then inputting t he vectors sequentially to VAE for imputation

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-07T05:07:27.844077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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