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Found 9 papers, 6 papers with code
TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models
The performance of testing newly incoming unseen time series on current TSAD algorithms remains unknown.
Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective
To ensure an accurate AD, FCVAE exploits an innovative approach to concurrently integrate both the global and local frequency features into the condition of Conditional Variational Autoencoder (CVAE) to significantly increase the accuracy of reconstructing the normal data.
InstructPix2NeRF: Instructed 3D Portrait Editing from a Single Image
With the success of Neural Radiance Field (NeRF) in 3D-aware portrait editing, a variety of works have achieved promising results regarding both quality and 3D consistency.
OpsEval: A Comprehensive IT Operations Benchmark Suite for Large Language Models
Information Technology (IT) Operations (Ops), particularly Artificial Intelligence for IT Operations (AIOps), is the guarantee for maintaining the orderly and stable operation of existing information systems.
Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly Detection
Massive key performance indicators (KPIs) are monitored as multivariate time series data (MTS) to ensure the reliability of the software applications and service system.
3D Brainformer: 3D Fusion Transformer for Brain Tumor Segmentation
By the nature of limited receptive fields, however, those architectures are subject to representing long-range spatial dependencies of the voxel intensities in MRI images.
PREIM3D: 3D Consistent Precise Image Attribute Editing from a Single Image
As for imprecise image editing, we attribute the problem to the gap between the latent space of real images and that of generated images.
oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation
We present oneDNN Graph Compiler, a tensor compiler that employs a hybrid approach of using techniques from both compiler optimization and expert-tuned kernels for high performance code generation of the deep neural network graph.
Heterogeneous Metric Learning with Content-based Regularization for Software Artifact Retrieval
Specifically, this method can capture both the inherent information in the source codes and the semantic information hidden in the comments, descriptions, and identifiers of the source codes.
