MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion | Paper Citation Record

Observation 8257333a-124c-4f70-b280-593a1d706da2 · outbound

This paper cites User-oriented virtual mobile network resource management for vehicle com- munications,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion User-oriented virtual mobile network resource management for vehicle com- munications,

Reference 1

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Observation 9353956e-6936-42f5-b6f2-0adb92168641 · outbound

This paper cites No-pain no-gain: Drl assisted optimization in energy-constrained cr-noma networks,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion No-pain no-gain: Drl assisted optimization in energy-constrained cr-noma networks,

Reference 2

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Observation c77db6e9-3080-4aa7-85d7-bd413d21954f · outbound

This paper cites Understanding and prediction of mobile application usage for smart phones,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Understanding and prediction of mobile application usage for smart phones,

Reference 3

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Observation ca80771b-9999-4e05-b455-83f9337bb7dc · outbound

This paper cites Deep learning on network traffic prediction: Recent advances, analysis, and future directions,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Deep learning on network traffic prediction: Recent advances, analysis, and future directions,

Reference 4

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Observation 0b0d312c-952f-4b79-8dbe-dae2910ead26 · outbound

This paper cites Practical gan-based synthetic ip header trace generation using netshare,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Practical gan-based synthetic ip header trace generation using netshare,

Reference 5

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Observation a51e01dc-aaf7-48c8-bd2b-e6dde1c17143 · outbound

This paper cites pcapstego: A tool for generating traffic traces for experimenting with network covert channels,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion pcapstego: A tool for generating traffic traces for experimenting with network covert channels,

Reference 6

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Observation 7a34e36c-a4ea-4baf-bf7a-3460da4a5538 · outbound

This paper cites Generative, high-fidelity network traces,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Generative, high-fidelity network traces,

Reference 7

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Observation 3c5ce872-e8c8-437c-9b64-13564c691679 · outbound

This paper cites Data driven prediction models of energy use of appliances in a low-energy house,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Data driven prediction models of energy use of appliances in a low-energy house,

Reference 8

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Observation e2bbbf51-474f-49bb-8884-3b887bcea3c2 · outbound

This paper cites Time-series generative adversarial networks,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Time-series generative adversarial networks,

Reference 9

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Observation 549bc0f7-c335-4d21-95c6-de653e2c2ca6 · outbound

This paper cites TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network

Reference 10

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Observation db94a9b5-ba7b-442c-a113-5037a6952ba4 · outbound

This paper cites Mobile phone use as sequential processes: From discrete behaviors to sessions of behaviors and trajectories of sessions,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Mobile phone use as sequential processes: From discrete behaviors to sessions of behaviors and trajectories of sessions,

Reference 11

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Observation 0c7eda27-8785-4ec9-a1cc-80c013a77c27 · outbound

This paper cites Circadian pattern and burstiness in mobile phone communication,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Circadian pattern and burstiness in mobile phone communication,

Reference 12

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Observation 6fba46c7-5352-4e4b-af99-d6cde82ebd68 · outbound

This paper cites Zero-inflated poisson regression, with an application to defects in manufacturing,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Zero-inflated poisson regression, with an application to defects in manufacturing,

Reference 13

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Observation 2e6c7017-f951-4bdb-87e1-3f91d1375cd5 · outbound

This paper cites Timevae: A variational auto-encoder for multivariate time series generation,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Timevae: A variational auto-encoder for multivariate time series generation,

Reference 14

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Observation f6c03611-63a0-4e38-b756-98b3d35e2e53 · outbound

This paper cites Diffusion-TS: Interpretable diffusion for general time series generation,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Diffusion-TS: Interpretable diffusion for general time series generation,

Reference 15

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Observation d5130a90-c05e-46c6-bb49-12e4ace49fc8 · outbound

This paper cites Knowledge enhanced gan for iot traffic generation,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Knowledge enhanced gan for iot traffic generation,

Reference 16

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Observation 539eab1f-b48f-4248-9b76-e4b6066ddb4e · outbound

This paper cites Spatio-temporal knowledge driven diffusion model for mobile traffic generation,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Spatio-temporal knowledge driven diffusion model for mobile traffic generation,

Reference 17

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Observation 3e62da76-87d0-4161-811b-882e5825d62f · outbound

This paper cites Netdiff: A service-guided hierarchical diffusion model for network flow trace generation,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Netdiff: A service-guided hierarchical diffusion model for network flow trace generation,

Reference 18

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Observation dcbd6faa-9ea8-46e8-96d8-abf4ed67834d · outbound

This paper cites AppGen: Mobility-aware App Usage Behavior Generation for Mobile Users.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion AppGen: Mobility-aware App Usage Behavior Generation for Mobile Users

Reference 19

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Observation 99bd8550-67d1-4367-9776-5a51b41df196 · outbound

This paper cites Multi-Time Attention Networks for Irregularly Sampled Time Series.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Multi-Time Attention Networks for Irregularly Sampled Time Series

Reference 20

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Observation b25a11a4-35fa-4c79-a539-61e0e3c53f44 · outbound

This paper cites Self-supervised transformer for sparse and irregularly sampled multivariate clinical time-series,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Self-supervised transformer for sparse and irregularly sampled multivariate clinical time-series,

Reference 21

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Observation 2f489224-a49e-487b-abe8-a55b2f39df90 · outbound

This paper cites Primenet: Pre-training for irregular multivariate time series,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Primenet: Pre-training for irregular multivariate time series,

Reference 22

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Observation dd1418d8-52df-4d21-830a-4b065768e5f7 · outbound

This paper cites Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts

Reference 23

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Observation 1eb9ab70-c2a1-4598-9b66-e65725d3e052 · outbound

This paper cites MTLComb: multi-task learning combining regression and classification tasks for joint feature selection.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion MTLComb: multi-task learning combining regression and classification tasks for joint feature selection

Reference 24

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Observation d3e64f9f-4169-4c73-9e96-10afef72a274 · outbound

This paper cites Addressing spatial-temporal heterogeneity: General mixed time series analysis via latent continuity recovery and align- ment,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Addressing spatial-temporal heterogeneity: General mixed time series analysis via latent continuity recovery and align- ment,

Reference 25

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Observation a2789f03-46e5-4ea6-a7a6-531a447b7654 · outbound

This paper cites General-purpose user embeddings based on mobile app usage,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion General-purpose user embeddings based on mobile app usage,

Reference 26

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Observation 5aaa6f45-7967-4943-8acf-ca8bc7d7ed34 · outbound

This paper cites Real World Longitudinal iOS App Usage Study at Scale.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Real World Longitudinal iOS App Usage Study at Scale

Reference 27

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Observation 93295c05-e532-4173-b466-f58e8e48df25 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion U-net: Convolutional networks for biomedical image segmentation,

Reference 28

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Observation 2804574d-ec90-4234-bc35-979bdd254fce · outbound

This paper cites Rotate to attend: Convolutional triplet attention module,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Rotate to attend: Convolutional triplet attention module,

Reference 29

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Observation 3f70d66b-1f7b-44a9-8bfb-ac62ced144c0 · outbound

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MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Unresolved cited work

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Observation 7840d9fb-28f3-4e39-b382-6e0516f2d433 · outbound

This paper cites Smartphone app usage prediction using points of interest,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Smartphone app usage prediction using points of interest,

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Observation aac8402d-cd3d-4789-9199-17c1f6af89f9 · outbound

This paper cites Generating multivariate time series with common source coordinated gan (cosci-gan),.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Generating multivariate time series with common source coordinated gan (cosci-gan),

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Observation 550d37b8-058d-422b-ae8c-ef969fb0a4e3 · outbound

This paper cites Variational Recurrent Auto-Encoders.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Variational Recurrent Auto-Encoders

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Observation 9580d5b8-2e33-4e85-b4c4-a99b0fd5cf35 · outbound

This paper cites Causal Recurrent Variational Autoencoder for Medical Time Series Generation.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Causal Recurrent Variational Autoencoder for Medical Time Series Generation

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Observation 2c476085-d762-47d6-b630-07135e635705 · outbound

This paper cites Population Aware Diffusion for Time Series Generation.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Population Aware Diffusion for Time Series Generation

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This paper cites Imaging time-series to improve classi- fication and imputation,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Imaging time-series to improve classi- fication and imputation,

Reference 36

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Observation 38d0d267-8a7f-49f0-811a-0151d7c4eb1c · outbound

This paper cites Netdiffus: Network traffic generation by diffusion models through time-series imaging,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Netdiffus: Network traffic generation by diffusion models through time-series imaging,

Reference 37

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Observation 00efa9ed-8f8e-414b-9a78-5a5282146c99 · outbound

This paper cites Utilizing image transforms and diffusion models for gen- erative modeling of short and long time series,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Utilizing image transforms and diffusion models for gen- erative modeling of short and long time series,

Reference 38

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Observation 356afb18-4e4d-4eca-8ff1-a8b0df03d5df · outbound

This paper cites Timesnet: Temporal 2d-variation modeling for general time series analysis,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Timesnet: Temporal 2d-variation modeling for general time series analysis,

Reference 39

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Observation 17fff72b-991b-49f9-a266-f5f43a987b77 · outbound

This paper cites Deep transfer learning for city-scale cellular traffic generation through urban knowledge graph,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Deep transfer learning for city-scale cellular traffic generation through urban knowledge graph,

Reference 40

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Observation 037db776-e0fe-4d87-a7e3-9f7775494d44 · outbound

This paper cites Diffusion model-based mobile traffic generation with open data for network planning and op- timization,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Diffusion model-based mobile traffic generation with open data for network planning and op- timization,

Reference 41

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Observation 806024bb-b837-40b5-a031-af3a0666d27d · outbound

This paper cites Spatio-temporal diffusion model for cellular traffic generation,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Spatio-temporal diffusion model for cellular traffic generation,

Reference 42

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Observation 555edd22-f4e8-4a5a-9e88-4cf57b800d83 · outbound

This paper cites Mobile user traffic generation via multi-scale hierarchical gan,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Mobile user traffic generation via multi-scale hierarchical gan,

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Observation 77cb8799-7f15-4ab7-a7cb-8e5eac4b2377 · outbound

This paper cites Packetdiff: A flow guided diffusion model for network packet trace generation,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Packetdiff: A flow guided diffusion model for network packet trace generation,

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Observation beb09dde-006c-4f42-b535-ee9f6ea04430 · outbound

This paper cites LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction

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Observation 9755c494-3122-4737-abf3-6d21fa87cf0f · outbound

This paper cites Improved Denoising Diffusion Probabilistic Models.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Improved Denoising Diffusion Probabilistic Models

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Observation 09a24869-eb2e-43af-91c4-e1a391f1fe5c · outbound

This paper cites Timeautodiff: A unified framework for generation, imputation, forecasting, and time-varying metadata conditioning of heterogeneous time series tabular data,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Timeautodiff: A unified framework for generation, imputation, forecasting, and time-varying metadata conditioning of heterogeneous time series tabular data,

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Observation de568518-9606-4977-99dc-b952308125d8 · outbound

This paper cites Zero-inflated time series gen- eration,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Zero-inflated time series gen- eration,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-02T02:44:19.564005Z

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Observation 696495cb-fd1e-403e-a417-99d44d242bb1 · outbound

This paper cites TSGBench: Time Series Generation Benchmark.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion TSGBench: Time Series Generation Benchmark

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Observation 0d9fbc45-f292-47ff-85f6-abe43e08176e · outbound

This paper cites Generative Adversarial Networks.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Generative Adversarial Networks

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Observation 00128ef0-b667-49d2-9453-1f384171ae73 · outbound

This paper cites Auto-encoding variational bayes,.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Auto-encoding variational bayes,

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Observation 0f363eee-be12-448b-a55c-f9e4362dae35 · outbound

This paper cites TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables

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Observation 85551db0-6084-455a-93ea-3cf0e27c52f3 · outbound

This paper cites Temporal Query Network for Efficient Multivariate Time Series Forecasting.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Temporal Query Network for Efficient Multivariate Time Series Forecasting

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Observation 976f0363-7470-4552-a418-392f3c317d7c · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Mamba: Linear-Time Sequence Modeling with Selective State Spaces

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Observation 5356cc8d-1366-41b1-8c4b-dc8dbad3bbd9 · outbound

This paper cites TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation

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Observation 4809048b-bc9a-4b8d-bd99-3595aa0a1072 · outbound

This paper cites Auto-Encoding Variational Bayes.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Auto-Encoding Variational Bayes

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Observation 68914760-331b-4833-b5c2-b12a483dd612 · outbound

This paper cites NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series Imaging.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series Imaging

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-02T02:44:18.794752Z

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