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Found 8 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.
Better Reasoning Behind Classification Predictions with BERT for Fake News Detection
Fake news detection has become a major task to solve as there has been an increasing number of fake news on the internet in recent years.
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.
Reinforcement Learning-Based Automatic Berthing System
However, because the ANN requires a large amount of training data to yield robust performance, the ANN-based automatic berthing system is somewhat limited due to the difficulty in obtaining the berthing data.
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.
