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Found 14 papers, 5 papers with code
Topology-Aware 3D Gaussian Splatting: Leveraging Persistent Homology for Optimized Structural Integrity
Gaussian Splatting (GS) has emerged as a crucial technique for representing discrete volumetric radiance fields.
Multimodal Relational Triple Extraction with Query-based Entity Object Transformer
Multimodal Relation Extraction is crucial for constructing flexible and realistic knowledge graphs.
Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting
We present a deep frequency derivative learning framework, DERITS, for non-stationary time series forecasting.
RTF: Region-based Table Filling Method for Relational Triple Extraction
Relational triple extraction is crucial work for the automatic construction of knowledge graphs.
Deep Coupling Network For Multivariate Time Series Forecasting
Multivariate time series (MTS) forecasting is crucial in many real-world applications.
TechGPT-2.0: A large language model project to solve the task of knowledge graph construction
Large language models have exhibited robust performance across diverse natural language processing tasks.
Frequency-domain MLPs are More Effective Learners in Time Series Forecasting
FreTS mainly involves two stages, (i) Domain Conversion, that transforms time-domain signals into complex numbers of frequency domain; (ii) Frequency Learning, that performs our redesigned MLPs for the learning of real and imaginary part of frequency components.
FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective
Multivariate time series (MTS) forecasting has shown great importance in numerous industries.
Graph Neural Network
Multivariate Time Series Forecasting
+1
SUGAR: Spherical Ultrafast Graph Attention Framework for Cortical Surface Registration
Cortical surface registration plays a crucial role in aligning cortical functional and anatomical features across individuals.
Few-Shot Nested Named Entity Recognition
Leveraging contextual dependency to distinguish nested entities, we propose a Biaffine-based Contrastive Learning (BCL) framework.
Edge-Varying Fourier Graph Networks for Multivariate Time Series Forecasting
The key problem in multivariate time series (MTS) analysis and forecasting aims to disclose the underlying couplings between variables that drive the co-movements.
A deep belief network-based method to identify proteomic risk markers for Alzheimer disease
Besides identifying a proteomic risk marker and further reinforce the link between metabolic risk factors and Alzheimer disease, this paper also suggests that apidonectin-linked pathways are a possible therapeutic drug target.
Deep ensemble learning for Alzheimers disease classification
The proposed deep ensemble learning framework is used for Alzheimers disease classification.
Secure Mobile Crowdsensing with Deep Learning
In order to stimulate secure sensing for Internet of Things (IoT) applications such as healthcare and traffic monitoring, mobile crowdsensing (MCS) systems have to address security threats, such as jamming, spoofing and faked sensing attacks, during both the sensing and the information exchange processes in large-scale dynamic and heterogenous networks.
