The infrastructure behind AI agents isn't static—it’s a living, evolving system. Designing effective data pipelines means ...

In the encoder stage of the VAE, both the VoxelEncoder and PlaneDownsampler classes perform downsampling. Similarly, in the decoder, both the VoxelDecoderBlock and PlaneUpsampler perform ...

Successful AI agents require enterprises to orchestrate interactions, manage shared knowledge and plan for failure.

The Architecture Growth Model (AGM) helps organisations improve Enterprise Architecture (EA) through a structured, research-backed approach.It supports incremental, balanced EA development aligned ...

In this paper, we propose a novel low-complex unsupervised model for anomaly detection (AD) within ST preprocessed LiDAR data named CNN-BiLSTM VAE that combines variational auto-encoder (VAE) ...

Abstract Anomaly detection in complex crowd scenes is a challenging task due to the inherent variability in crowd behaviors, interactions, and scales. This paper proposes a novel hybrid model that ...

Researchers are developing increasingly robust molecular representations, motivating the need for thorough methods to stress-test and validate them. Here, we use a variational auto-encoder (VAE), an ...

In the Fundamentals of the Model Architecture section, we explain Convolutional Neural Network architectures, show relevant works within the Fault Detection and Diagnosis context, and detail the ...

To improve the accuracy of anomaly detection under unbalanced sample conditions, we propose a new semi-supervised anomaly detection method (WCOS) based on semi-supervised clustering, which combines ...