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Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction
Objective: Multi-modal functional magnetic resonance imaging (fMRI) can ...
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Learning Disentangled Semantic Representation for Domain Adaptation
Domain adaptation is an important but challenging task. Most of the exis...
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Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation
Reinforcement learning (RL) algorithms usually require a substantial amo...
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R^2-Net: Relation of Relation Learning Network for Sentence Semantic Matching
Sentence semantic matching is one of the fundamental tasks in natural la...
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Learning Heatmap-Style Jigsaw Puzzles Provides Good Pretraining for 2D Human Pose Estimation
The target of 2D human pose estimation is to locate the keypoints of bod...
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How Do Fair Decisions Fare in Long-term Qualification?
Although many fairness criteria have been proposed for decision making, ...
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Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs
Causal discovery aims to recover causal structures or models underlying ...
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Attack on Multi-Node Attention for Object Detection
This paper focuses on high-transferable adversarial attacks on detection...
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Adaptive Task Sampling for Meta-Learning
Meta-learning methods have been extensively studied and applied in compu...
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On the Role of Sparsity and DAG Constraints for Learning Linear DAGs
Learning graphical structure based on Directed Acyclic Graphs (DAGs) is ...
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Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach
In many recommender systems, users and items are associated with attribu...
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A Causal View on Robustness of Neural Networks
We present a causal view on the robustness of neural networks against in...
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Learning from Positive and Unlabeled Data by Identifying the Annotation Process
In binary classification, Learning from Positive and Unlabeled data (LeP...
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Domain Adaptation As a Problem of Inference on Graphical Models
This paper is concerned with data-driven unsupervised domain adaptation,...
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Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach
Graph Convolutional Networks (GCNs) are state-of-the-art graph based rep...
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Tracing the Propagation Path: A Flow Perspective of Representation Learning on Graphs
Graph Convolutional Networks (GCNs) have gained significant developments...
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Transfer Learning-Based Outdoor Position Recovery with Telco Data
Telecommunication (Telco) outdoor position recovery aims to localize out...
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Deep Physiological State Space Model for Clinical Forecasting
Clinical forecasting based on electronic medical records (EMR) can uncov...
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Disentanglement Challenge: From Regularization to Reconstruction
The challenge of learning disentangled representation has recently attra...
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Modelling EHR timeseries by restricting feature interaction
Time series data are prevalent in electronic health records, mostly in t...
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Characterizing Distribution Equivalence for Cyclic and Acyclic Directed Graphs
The main way for defining equivalence among acyclic directed graphs is b...
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DNANet: De-Normalized Attention Based Multi-Resolution Network for Human Pose Estimation
Recently, multi-resolution networks (such as Hourglass, CPN, HRNet, etc....
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Adversarial Orthogonal Regression: Two non-Linear Regressions for Causal Inference
We propose two nonlinear regression methods, named Adversarial Orthogona...
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Likelihood-Free Overcomplete ICA and Applications in Causal Discovery
Causal discovery witnessed significant progress over the past decades. I...
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Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables
We consider the problem of learning causal models from observational dat...
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Identification of Effective Connectivity Subregions
Standard fMRI connectivity analyses depend on aggregating the time serie...
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Domain Generalization via Multidomain Discriminant Analysis
Domain generalization (DG) aims to incorporate knowledge from multiple s...
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Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach
Majority of state-of-the-art monocular depth estimation methods are supe...
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Twin Auxiliary Classifiers GAN
Conditional generative models enjoy remarkable progress over the past fe...
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Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation
Discovery of causal relations from observational data is essential for m...
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Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models
In many scientific fields, such as economics and neuroscience, we are of...
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Causal Discovery with Cascade Nonlinear Additive Noise Models
Identification of causal direction between a causal-effect pair from obs...
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Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA
We consider the problem of inferring causal relationships between two or...
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Generative-Discriminative Complementary Learning
Majority of state-of-the-art deep learning methods for vision applicatio...
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Causal Discovery and Hidden Driving Force Estimation from Nonstationary/Heterogeneous Data
It is commonplace to encounter nonstationary or heterogeneous data. Such...
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On Learning Invariant Representation for Domain Adaptation
Due to the ability of deep neural nets to learn rich representations, re...
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Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data
Autism spectrum disorder (ASD) is one of the major developmental disorde...
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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Gliomas are the most common primary brain malignancies, with different d...
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Random Occlusion-recovery for Person Re-identification
As a basic task of multi-camera surveillance system, person re-identific...
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Geometry-Consistent Adversarial Networks for One-Sided Unsupervised Domain Mapping
Unsupervised domain mapping aims at learning a function to translate dom...
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Causal discovery in the presence of missing data
Missing data are ubiquitous in many domains such as healthcare. Dependin...
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User-Sensitive Recommendation Ensemble with Clustered Multi-Task Learning
This paper considers recommendation algorithm ensembles in a user-sensit...
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Causal Generative Domain Adaptation Networks
We propose a new generative model for domain adaptation, in which traini...
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Scalable and accurate deep learning for electronic health records
Predictive modeling with electronic health record (EHR) data is anticipa...
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Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation
This paper is concerned with how to make efficient use of social informa...
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Causality Refined Diagnostic Prediction
Applying machine learning in the health care domain has shown promising ...
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Transfer Learning with Label Noise
Transfer learning aims to improve learning in the target domain with lim...
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Causal Discovery in the Presence of Measurement Error: Identifiability Conditions
Measurement error in the observed values of the variables can greatly ch...
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Learning Causal Structures Using Regression Invariance
We study causal inference in a multi-environment setting, in which the f...
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A New Measure of Conditional Dependence
Measuring conditional dependencies among the variables of a network is o...
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