Temporal Attention Bottleneck is informative? Interpretability through Disentangled Generative Representations for Energy Time Series Disaggregation
Generative models have garnered significant attention for their ability to address the challenge of source separation in disaggregation tasks. This approach holds promise for promoting energy conservation by enabling homeowners to obtain detailed information on their energy consumption solely through the analysis of aggregated load curves. Nevertheless, the model's ability to generalize and its interpretability remain two major challenges. To tackle these challenges, we deploy a generative model called TAB-VAE (Temporal Attention Bottleneck for Variational Auto-encoder), based on hierarchical architecture, addresses signature variability, and provides a robust, interpretable separation through the design of its informative representation of latent space. Our implementation and evaluation guidelines are available at https://github.com/oublalkhalid/TAB-VAE.
Code (1)
Tasks
Time SeriesSimilar Papers 제목 키워드 기반
SPaRSe-TIME: Saliency-Projected Low-Rank Temporal Modeling for Efficient and Interpretable Time Series Prediction
Time series forecasting is traditionally dominated by sequence-based architectures such as recurrent neural networks and attention mechanisms, which process all time steps uniformly and often incur substantial computatio…
Time Series ForecastingTime Series PredictionTree-Based Leakage Inspection and Control in Concept Bottleneck Models
As AI models grow larger, the demand for accountability and interpretability has become increasingly critical for understanding their decision-making processes. Concept Bottleneck Models (CBMs) have gained attention for …
Decision MakingD-GATNet: Interpretable Temporal Graph Attention Learning for ADHD Identification Using Dynamic Functional Connectivity
Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder whose neuroimaging-based diagnosis remains challenging due to complex time-varying disruptions in brain connectivity. Functional …
Hypergraph and Latent ODE Learning for Multimodal Root Cause Localization in Microservices
Root cause localization in cloud native microservice systems requires modeling complex service dependencies, irregular temporal dynamics, and heterogeneous observability data. We present HyperODE RCA, a unified framework…
GINTRIP: Interpretable Temporal Graph Regression using Information bottleneck and Prototype-based method
Deep neural networks (DNNs) have demonstrated remarkable performance across various domains, yet their application to temporal graph regression tasks faces significant challenges regarding interpretability. This critical…
Graph RegressionMulti-Task Learningregression