Hugging Face Trending Papers August 4, 2026

SynEnergy: Anomaly Semantic-Guided Diffusion for Synthetic Energy Data Generation

Read the original on Hugging Face Trending Papers →

Fine-grained energy consumption data are essential for applications such as demand forecasting, demand response planning, and grid reliability assessment. However, access to such data is often restricted by privacy concerns and data-sharing constraints, motivating growing interest in synthetic energy data generation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Hugging Face Trending Papers.

arXiv AI

Aug 5

SynEnergy: Anomaly Semantic-Guided Diffusion for Synthetic Energy Data Generation

arXiv:2608. 03087v1 Announce Type: cross Abstract: Fine-grained energy consumption data are essential for applications such as demand forecasting, demand response planning, and grid reliability assessment.

By Lin Jiang, Dahai Yu, Ravikumar Gelli, Guang Wang

diffusion safety

arXiv Machine Learning

Sep 24

CAST: Context- and Anomaly Structure-Conditioned Time Series Anomaly Generation

CAST is a framework for generating anomalous time series that addresses the scarcity and heterogeneity of anomaly data. It uses a two‑stage approach: pretraining on abundant normal data to learn system dynamics, then finetuning with anomaly structure representations to capture diverse anomaly morphologies. Experiments on real‑world datasets show that CAST outperforms existing methods in both generation quality and downstream task performance.

By Haochen Zhang, Jie Peng, Songyuan Sui, Yu-Chao Huang, Xiangqi Zhu, Tianlong Chen

fine-tuning benchmarks safety

arXiv AI

Jun 2

EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction

arXiv:2606. 00506v1 Announce Type: new Abstract: Energy consumption prediction is essential for efficient grid management, demand-side optimization, and sustainable energy planning.

By Dahai Yu, Rongchao Xu, Lin Jiang, Guang Wang

benchmarks

arXiv Machine Learning

Jun 2

Modeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detection

arXiv:2606. 00304v1 Announce Type: new Abstract: Graph anomaly detection methods aim to distinguish anomalous nodes.

By Yilin Liu, Hongchao Zhang, Taylor T. Johnson, Ahmad F. Taha, Meiyi Ma

benchmarks

arXiv Machine Learning

Jun 9

Anomaly-Preference Image Generation

arXiv:2605. 02439v3 Announce Type: replace-cross Abstract: Synthesizing realistic and diverse anomalous samples from limited data is vital for robust model generalization.

By Fuyun Wang, Yuanzhi Wang, Xu Guo, Sujia Huang, Tong Zhang, Dan Wang, Hui Yan, Xin Liu, Zhen Cui

diffusion benchmarks safety

arXiv Machine Learning

Sep 4

Statistical Feature Augmentation for Anomaly Detection in Dynamic Graphs

The paper introduces a statistical feature augmentation technique that encodes behavioral interaction statistics into the input space for dynamic graph anomaly detection. Experiments on Reddit, Wikipedia, and MOOC datasets across seven models—both continuous-time and discrete-time—show that this augmentation consistently improves detection performance compared to models trained on original embeddings. The enriched input also facilitates fine-grained post-hoc analysis of behavioral importance, linking classical network analysis with deep learning.

By Philipp Schlinge, Jean-Luc Schnipper, Martin Atzmueller

rag benchmarks

One email a morning, machine-written

Related stories

SynEnergy: Anomaly Semantic-Guided Diffusion for Synthetic Energy Data Generation

CAST: Context- and Anomaly Structure-Conditioned Time Series Anomaly Generation

EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction

Modeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detection

Anomaly-Preference Image Generation

Statistical Feature Augmentation for Anomaly Detection in Dynamic Graphs