7.1k citations
- A. Alikhanyan National LaboratoryAM283 papers
- University of VirginiaUS280 papers
- Carnegie Mellon UniversityUS260 papers
- Kyungpook National UniversityKR260 papers
- Florida State UniversityUS254 papers
- Massachusetts Institute of TechnologyUS249 papers
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR240 papers
- CEA Paris-SaclayFR239 papers
- Lomonosov Moscow State UniversityRU239 papers
- University of Maryland, College ParkUS239 papers
- Centre National de la Recherche ScientifiqueFR237 papers
- Rutgers, The State University of New JerseyUS233 papers
7 papers · 1 filter
Integrating Attention-Enhanced LSTM and Particle Swarm Optimization for Dynamic Pricing and Replenishment Strategies in Fresh Food Supermarkets
Xianchen Liu, Tianhui Zhang, Xinyu Zhang +4
This paper presents a novel approach to optimizing pricing and replenishment strategies in fresh food supermarkets by combining Long Short-Term Memory (LSTM) networks with Particle…
Retrieval-Augmented Foundation Models for Water Level Prediction in the Everglades
Rahuul Rangaraj, Jimeng Shi, Rajendra Paudel +2
Accurate water level forecasting in the Everglades is essential for flood mitigation, drought management, water resource planning, and biodiversity conservation. While recent time-…
Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks
Jiaxing Zhang, Xiaoou Liu, Dongsheng Luo +1
Explaining Graph Neural Networks (GNNs) has garnered significant attention due to the need for interpretability, enabling users to understand the behavior of these black-box models…
Multi-source Unsupervised Domain Adaptation on Graphs with Transferability Modeling
Tianxiang Zhao, Dongsheng Luo, Xiang Zhang +1
In this paper, we tackle a new problem of \textit{multi-source unsupervised domain adaptation (MSUDA) for graphs}, where models trained on annotated source domains need to be trans…
Hierarchical Pruning of Deep Ensembles with Focal Diversity
Yanzhao Wu, Ka-Ho Chow, Wenqi Wei +1
Deep neural network ensembles combine the wisdom of multiple deep neural networks to improve the generalizability and robustness over individual networks. It has gained increasing…
MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation
Jiaxing Zhang, Dongsheng Luo, Hua Wei
Graph Neural Networks (GNNs) have received increasing attention due to their ability to learn from graph-structured data. However, their predictions are often not interpretable. Po…