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Found 24 papers, 8 papers with code
Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators
Recently, channel-independent methods have achieved state-of-the-art performance in multivariate time series (MTS) forecasting.
Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network
Heterogeneous graph neural networks (HGNs) are prominent approaches to node classification tasks on heterogeneous graphs.
SSIN: Self-Supervised Learning for Rainfall Spatial Interpolation
Inspired by the Cloze task and BERT, we fully consider the characteristics of spatial interpolation and design the SpaFormer model based on the Transformer architecture as the core of SSIN.
Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey
Predicting stock prices presents a challenging research problem due to the inherent volatility and non-linear nature of the stock market.
DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting
To address this challenge, we propose DoubleAdapt, an end-to-end framework with two adapters, which can effectively adapt the data and the model to mitigate the effects of distribution shifts.
RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction
Specifically, we employ a shared predictor to infer basis user preferences, which acquires global preference knowledge from the interactions of different users.
Dynamic Community Detection via Adversarial Temporal Graph Representation Learning
Dynamic community detection has been prospered as a powerful tool for quantifying changes in dynamic brain network connectivity patterns by identifying strongly connected sets of nodes.
Morphological feature visualization of Alzheimer's disease via Multidirectional Perception GAN
In this work, a novel Multidirectional Perception Generative Adversarial Network (MP-GAN) is proposed to visualize the morphological features indicating the severity of AD for patients of different stages.
Fault Diagnosis of Nonlinear Systems Using a Hybrid-Degree Dual Cubature-based Estimation Scheme
In this paper, a novel hybrid-degree dual estimation approach based on cubature rules and cubature-based nonlinear filters is proposed for fault diagnosis of nonlinear systems through simultaneous state and time-varying parameter estimation.
DecGAN: Decoupling Generative Adversarial Network detecting abnormal neural circuits for Alzheimer's disease
In this work, a novel decoupling generative adversarial network (DecGAN) is proposed to detect abnormal neural circuits for AD.
A Prior Guided Adversarial Representation Learning and Hypergraph Perceptual Network for Predicting Abnormal Connections of Alzheimer's Disease
The proposed model can evaluate characteristics of abnormal brain connections at different stages of Alzheimer's disease, which is helpful for cognitive disease study and early treatment.
Resolving Training Biases via Influence-based Data Relabeling
To achieve this, we use influence functions to estimate how relabeling a training sample would affect model's test performance and further develop a novel relabeling function R. We theoretically prove that applying R to relabel harmful training samples allows the model to achieve lower test loss than simply discarding them for any classification tasks using cross-entropy loss.
3D Brain Reconstruction by Hierarchical Shape-Perception Network from a Single Incomplete Image
A branching predictor and several hierarchical attention pipelines are constructed to generate point clouds that accurately describe the incomplete images and then complete these point clouds with high quality.
A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction
Fusing medical images and the corresponding 3D shape representation can provide complementary information and microstructure details to improve the operational performance and accuracy in brain surgery.
Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Prediction
Then two hypergraphs are constructed from the latent representations and the adversarial network based on graph convolution is employed to narrow the distribution difference of hyperedge features.
Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer's Disease Analysis
Using multimodal neuroimaging data to characterize brain network is currently an advanced technique for Alzheimer's disease(AD) Analysis.
Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesis
Fusing multi-modality medical images, such as MR and PET, can provide various anatomical or functional information about human body.
Tensorizing GAN with High-Order Pooling for Alzheimer's Disease Assessment
To the best of our knowledge, the proposed Tensor-train, High-pooling and Semi-supervised learning based GAN (THS-GAN) is the first work to deal with classification on MRI images for AD diagnosis.
Differentiable Neural Input Search for Recommender Systems
The dimensions of different feature embeddings are often set to a same value empirically, which limits the predictive performance of latent factor models.
Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions
Various factorization-based methods have been proposed to leverage second-order, or higher-order cross features for boosting the performance of predictive models.
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Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis
The results demonstrate the effectiveness and efficiency of FIA, and the usefulness of the generated explanations for the recommendation results.
Revisiting Flow Information for Traffic Prediction
However, existing traffic prediction methods focus on modeling complex spatiotemporal traffic correlations and seldomly study the influence of the original traffic flows among regions.
Explaining Latent Factor Models for Recommendation with Influence Functions
Latent factor models (LFMs) such as matrix factorization achieve the state-of-the-art performance among various Collaborative Filtering (CF) approaches for recommendation.
A Neural Attention Model for Urban Air Quality Inference: Learning the Weights of Monitoring Stations
We leverage both the information from monitoring stations and urban data that are closely related to air quality, including POIs, road networks and meteorology.
