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Found 10 papers, 3 papers with code
EffiCANet: Efficient Time Series Forecasting with Convolutional Attention
The exponential growth of multivariate time series data from sensor networks in domains like industrial monitoring and smart cities requires efficient and accurate forecasting models.
Towards Universal Large-Scale Foundational Model for Natural Gas Demand Forecasting
In the context of global energy strategy, accurate natural gas demand forecasting is crucial for ensuring efficient resource allocation and operational planning.
Towards Better Graph-based Cross-document Relation Extraction via Non-bridge Entity Enhancement and Prediction Debiasing
However, these studies ignore the non-bridge entities, each of which co-occurs with only one target entity and offers the semantic association between target entities for relation prediction.
Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal
When conducting continual learning based on a publicly-released LLM checkpoint, the availability of the original training data may be non-existent.
Wasserstein Differential Privacy
We propose Wasserstein differential privacy (WDP), an alternative DP framework to measure the risk of privacy leakage, which satisfies the properties of symmetry and triangle inequality.
HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling for Long-Term Forecasting
Time series forecasting is crucial and challenging in the real world.
Ranked #18 on
Time Series Forecasting
on ETTh1 (336) Multivariate
Federated Learning in Big Model Era: Domain-Specific Multimodal Large Models
Preliminary experiments show that enterprises can enhance and accumulate intelligent capabilities through multimodal model federated learning, thereby jointly creating an smart city model that provides high-quality intelligent services covering energy infrastructure safety, residential community security, and urban operation management.
The Prospect of Enhancing Large-Scale Heterogeneous Federated Learning with Transformers
Federated learning (FL) addresses data privacy concerns by enabling collaborative training of AI models across distributed data owners.
Hierarchical Federated Learning Incentivization for Gas Usage Estimation
Federated learning (FL) offers a solution to this problem by enabling local data processing on each participant, such as gas companies and heating stations.
Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout
Artificial intelligence (AI)-empowered industrial fault diagnostics is important in ensuring the safe operation of industrial applications.
