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Found 9 papers, 2 papers with code
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.
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.
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.
Deep ensemble learning for Alzheimers disease classification
The proposed deep ensemble learning framework is used for Alzheimers disease classification.
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.
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.
Few-Shot Nested Named Entity Recognition
Leveraging contextual dependency to distinguish nested entities, we propose a Biaffine-based Contrastive Learning (BCL) framework.
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.
Deep Coupling Network For Multivariate Time Series Forecasting
Multivariate time series (MTS) forecasting is crucial in many real-world applications.
