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Found 18 papers, 2 papers with code
UMR-Writer: A Web Application for Annotating Uniform Meaning Representations
We present UMR-Writer, a web-based application for annotating Uniform Meaning Representations (UMR), a graph-based, cross-linguistically applicable semantic representation developed recently to support the development of interpretable natural language applications that require deep semantic analysis of texts.
LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data
By leveraging model partitioning and adopting distinct aggregation strategies for each sub-model, LCFed effectively incorporates global knowledge into intra-cluster co-training, achieving optimal training performance.
KACDP: A Highly Interpretable Credit Default Prediction Model
In conclusion, the KACDP model constructed in this paper exhibits excellent predictive performance and satisfactory interpretability in individual credit risk prediction, providing an effective way to address the limitations of existing methods and offering a new and practical credit risk prediction tool for financial institutions.
Neural Network-based High-index Saddle Dynamics Method for Searching Saddle Points and Solution Landscape
The high-index saddle dynamics (HiSD) method is a powerful approach for computing saddle points and solution landscape.
Resilience-Oriented DG Siting and Sizing Considering Energy Equity Constraint
Then, the DG siting and sizing problem is formulated as a two-stage stochastic programming with the EEC.
SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework
Despite this potential, the limited satellite-ground communication bandwidth and the heterogeneous operating environments of ground devices-including variations in data, bandwidth, and computing power-pose substantial challenges for effective and robust satellite-assisted FL.
PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding
Network embedding has numerous practical applications and has received extensive attention in graph learning, which aims at mapping vertices into a low-dimensional and continuous dense vector space by preserving the underlying structural properties of the graph.
Exploring Lightweight Federated Learning for Distributed Load Forecasting
Federated Learning (FL) is a distributed learning scheme that enables deep learning to be applied to sensitive data streams and applications in a privacy-preserving manner.
A Survey on Large Language Models from Concept to Implementation
Recent advancements in Large Language Models (LLMs), particularly those built on Transformer architectures, have significantly broadened the scope of natural language processing (NLP) applications, transcending their initial use in chatbot technology.
FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data
Clustered federated learning (CFL) is proposed to mitigate the performance deterioration stemming from data heterogeneity in federated learning (FL) by grouping similar clients for cluster-wise model training.
Deep Learning-Assisted Simultaneous Targets Sensing and Super-Resolution Imaging
In this study, a multifunctional deep neural network is demonstrated to reconstruct target information in a metasurface targets interactive system.
GIDN: A Lightweight Graph Inception Diffusion Network for High-efficient Link Prediction
In this paper, we propose a Graph Inception Diffusion Networks(GIDN) model.
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Deep Reinforcement Learning based Model-free On-line Dynamic Multi-Microgrid Formation to Enhance Resilience
The proposed deep RL method provides real-time computing to support on-line dynamic MMGF scheme, and the scheme handles a long-term resilience enhancement problem using adaptive on-line MMGF to defend changeable conditions.
Deep Learning based Model-free Robust Load Restoration to Enhance Bulk System Resilience with Wind Power Penetration
This paper proposes a new deep learning (DL) based model-free robust method for bulk system on-line load restoration with high penetration of wind power.
Factuality Assessment as Modal Dependency Parsing
In this paper, we frame factuality assessment as a modal dependency parsing task that identifies the events and their sources, formally known as conceivers, and then determine the level of certainty that the sources are asserting with respect to the events.
Learning Structural Graph Layouts and 3D Shapes for Long Span Bridges 3D Reconstruction
Considering the prior human knowledge that these structures are in conformity to regular spatial layouts in terms of components, a learning-based topology-aware 3D reconstruction method which can obtain high-level structural graph layouts and low-level 3D shapes from images is proposed in this paper.
Toward Packet Routing with Fully-distributed Multi-agent Deep Reinforcement Learning
Packet routing is one of the fundamental problems in computer networks in which a router determines the next-hop of each packet in the queue to get it as quickly as possible to its destination.
