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Found 9 papers, 6 papers with code
Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling
We present a novel time series anomaly detection method that achieves excellent detection accuracy while offering a superior level of explainability.
Ranked #1 on
Time Series Anomaly Detection
on UCR Anomaly Archive
Latent Diffusion Model for Conditional Reservoir Facies Generation
Creating accurate and geologically realistic reservoir facies based on limited measurements is crucial for field development and reservoir management, especially in the oil and gas sector.
Masked Generative Modeling with Enhanced Sampling Scheme
ESS explicitly ensures both sample diversity and fidelity, and consists of three stages: Naive Iterative Decoding, Critical Reverse Sampling, and Critical Resampling.
Vector Quantized Time Series Generation with a Bidirectional Prior Model
Time series generation (TSG) studies have mainly focused on the use of Generative Adversarial Networks (GANs) combined with recurrent neural network (RNN) variants.
Persistence Initialization: A novel adaptation of the Transformer architecture for Time Series Forecasting
We also compare against existing recently proposed Transformer models for time series forecasting, showing superior performance on the M4 dataset.
VNIbCReg: VICReg with Neighboring-Invariance and better-Covariance Evaluated on Non-stationary Seismic Signal Time Series
This study shows that a combination of a VICReg-style method and TNC is very effective for SSL on non-stationary time series, where a non-stationary seismic signal time series is used as an evaluation dataset.
Computer Vision Self-supervised Learning Methods on Time Series
Our method improves on a \textit{covariance} term proposed in VICReg, and in addition we augment the head of the architecture by an iterative normalization layer that accelerates the convergence of the model.
Augmented Memory Networks for Streaming-Based Active One-Shot Learning
In stream-based AL, observations are continuously made available to the learner that have to decide whether to request a label or to make a prediction.
Time is of the Essence: a Joint Hierarchical RNN and Point Process Model for Time and Item Predictions
In this work we combine these two extensions in a joint model for the tasks of recommendation and return-time prediction.
