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08/09/2023
Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey
Predicting stock prices presents a challenging research problem due to t...
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06/16/2023
DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting
Stock trend forecasting is a fundamental task of quantitative investment...
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10/28/2022
RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction
Click-Through Rate (CTR) prediction on cold users is a challenging task ...
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11/25/2021
Morphological feature visualization of Alzheimer's disease via Multidirectional Perception GAN
The diagnosis of early stages of Alzheimer's disease (AD) is essential f...
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10/12/2021
A Prior Guided Adversarial Representation Learning and Hypergraph Perceptual Network for Predicting Abnormal Connections of Alzheimer's Disease
Alzheimer's disease is characterized by alterations of the brain's struc...
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10/12/2021
DecGAN: Decoupling Generative Adversarial Network detecting abnormal neural circuits for Alzheimer's disease
One of the main reasons for Alzheimer's disease (AD) is the disorder of ...
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07/21/2021
Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer's Disease Analysis
Using multimodal neuroimaging data to characterize brain network is curr...
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07/21/2021
Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Prediction
Multimodal neuroimage can provide complementary information about the de...
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07/21/2021
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 ...
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08/08/2020
Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesis
Fusing multi-modality medical images, such as MR and PET, can provide va...
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08/03/2020
Tensorizing GAN with High-Order Pooling for Alzheimer's Disease Assessment
It is of great significance to apply deep learning for the early diagnos...
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06/08/2020
Differentiable Neural Input Search for Recommender Systems
Latent factor models are the driving forces of the state-of-the-art reco...
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09/07/2019
Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions
Various factorization-based methods have been proposed to leverage secon...
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06/03/2019
Revisiting Flow Information for Traffic Prediction
Traffic prediction is a fundamental task in many real applications, whic...
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12/12/2018
STEP : A Distributed Multi-threading Framework Towards Efficient Data Analytics
Various general-purpose distributed systems have been proposed to cope w...
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11/20/2018
Explaining Latent Factor Models for Recommendation with Influence Functions
Latent factor models (LFMs) such as matrix factorization achieve the sta...
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11/01/2018
Modeling Conceptual Characteristics of Virtual Machines for CPU Utilization Prediction
Cloud services have grown rapidly in recent years, which provide high fl...
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09/27/2016
