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11/13/2022
Normative Modeling via Conditional Variational Autoencoder and Adversarial Learning to Identify Brain Dysfunction in Alzheimer's Disease
Normative modeling is an emerging and promising approach to effectively ...
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07/06/2022
Learning Apparent Diffusion Coefficient Maps from Undersampled Radial k-Space Diffusion-Weighted MRI in Mice using a Deep CNN-Transformer Model in Conjunction with a Monoexpone
Purpose: To accelerate radially sampled diffusion weighted spin-echo (Ra...
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10/14/2020
AutoADR: Automatic Model Design for Ad Relevance
Large-scale pre-trained models have attracted extensive attention in the...
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02/07/2018
Learning Role-based Graph Embeddings
Random walks are at the heart of many existing network embedding methods...
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10/27/2017
Similarity-based Multi-label Learning
Multi-label classification is an important learning problem with many ap...
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10/25/2017
Inductive Representation Learning in Large Attributed Graphs
Graphs (networks) are ubiquitous and allow us to model entities (nodes) ...
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09/14/2017
A Framework for Generalizing Graph-based Representation Learning Methods
Random walks are at the heart of many existing deep learning algorithms ...
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04/28/2017
Deep Feature Learning for Graphs
This paper presents a general graph representation learning framework ca...
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01/06/2017
Estimation of Graphlet Statistics
Graphlets are induced subgraphs of a large network and are important for...
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10/04/2016
Revisiting Role Discovery in Networks: From Node to Edge Roles
Previous work in network analysis has focused on modeling the mixed-memb...
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08/18/2016
Hybrid CPU-GPU Framework for Network Motifs
Massively parallel architectures such as the GPU are becoming increasing...
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08/02/2016
Relational Similarity Machines
This paper proposes Relational Similarity Machines (RSM): a fast, accura...
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01/16/2014
On-line Planning and Scheduling: An Application to Controlling Modular Printers
We present a case study of artificial intelligence techniques applied to...
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01/16/2014