
Dirichlet Energy Constrained Learning for Deep Graph Neural Networks
Graph neural networks (GNNs) integrate deep architectures and topologica...
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Generating the Graph Gestalt: KernelRegularized Graph Representation Learning
Recent work on graph generative models has made remarkable progress towa...
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Fairness via Representation Neutralization
Existing bias mitigation methods for DNN models primarily work on learni...
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ModelBased Counterfactual Synthesizer for Interpretation
Counterfactuals, serving as one of the emerging type of model interpreta...
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DouZero: Mastering DouDizhu with SelfPlay Deep Reinforcement Learning
Games are abstractions of the real world, where artificial agents learn ...
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A General Taylor Framework for Unifying and Revisiting Attribution Methods
Attribution methods provide an insight into the decisionmaking process ...
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Learning Disentangled Representations for Time Series
Timeseries representation learning is a fundamental task for timeserie...
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Mutual Information Preserving Backpropagation: Learn to Invert for Faithful Attribution
Back propagation based visualizations have been proposed to interpret de...
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DivAug: Plugin Automated Data Augmentation with Explicit Diversity Maximization
Humandesigned data augmentation strategies have been replaced by automa...
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Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU models
Recent studies indicate that NLU models are prone to rely on shortcut fe...
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Model Complexity of Deep Learning: A Survey
Model complexity is a fundamental problem in deep learning. In this pape...
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Dynamic Memory based Attention Network for Sequential Recommendation
Sequential recommendation has become increasingly essential in various o...
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SparseInterest Network for Sequential Recommendation
Recent methods in sequential recommendation focus on learning an overall...
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Rank the Episodes: A Simple Approach for Exploration in ProcedurallyGenerated Environments
Exploration under sparse reward is a longstanding challenge of modelfr...
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Generative Counterfactuals for Neural Networks via AttributeInformed Perturbation
With the wide use of deep neural networks (DNN), model interpretability ...
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Deep Serial Number: Computational Watermarking for DNN Intellectual Property Protection
In this paper, we introduce DSN (Deep Serial Number), a new watermarking...
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Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection
Dimensionality reduction is a crucial first step for many unsupervised l...
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Towards Interaction Detection Using Topological Analysis on Neural Networks
Detecting statistical interactions between input features is a crucial a...
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Are Interpretations Fairly Evaluated? A Definition Driven Pipeline for PostHoc Interpretability
Recent years have witnessed an increasing number of interpretation metho...
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A Unified Taylor Framework for Revisiting Attribution Methods
Attribution methods have been developed to understand the decision makin...
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Explainable Recommender Systems via Resolving Learning Representations
Recommender systems play a fundamental role in web applications in filte...
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Machine Learning Explanations to Prevent Overtrust in Fake News Detection
Combating fake news and misinformation propagation is a challenging task...
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Towards Automated Neural Interaction Discovery for ClickThrough Rate Prediction
ClickThrough Rate (CTR) prediction is one of the most important machine...
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AutoRec: An Automated Recommender System
Realistic recommender systems are often required to adapt to everchangi...
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AutoOD: Automated Outlier Detection via Curiosityguided Search and Selfimitation Learning
Outlier detection is an important data mining task with numerous practic...
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Measuring Model Complexity of Neural Networks with Curve Activation Functions
It is fundamental to measure model complexity of deep neural networks. T...
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Mitigating Gender Bias in Captioning Systems
Image captioning has made substantial progress with huge supporting imag...
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An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks
With the widespread use of deep neural networks (DNNs) in highstake app...
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Towards Deeper Graph Neural Networks with Differentiable Group Normalization
Graph neural networks (GNNs), which learn the representation of a node b...
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XGNN: Towards ModelLevel Explanations of Graph Neural Networks
Graphs neural networks (GNNs) learn node features by aggregating and com...
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Adversarial Machine Learning: An Interpretation Perspective
Recent years have witnessed the significant advances of machine learning...
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PyODDS: An Endtoend Outlier Detection System with Automated Machine Learning
Outlier detection is an important task for various data mining applicati...
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Learning to Hash with Graph Neural Networks for Recommender Systems
Graph representation learning has attracted much attention in supporting...
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MultiChannel Graph Convolutional Networks
Graph neural networks (GNN) has been demonstrated to be effective in cla...
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XDeep: An Interpretation Tool for Deep Neural Networks
XDeep is an opensource Python package developed to interpret deep model...
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RLCard: A Toolkit for Reinforcement Learning in Card Games
RLCard is an opensource toolkit for reinforcement learning research in ...
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PyODDS: An EndtoEnd Outlier Detection System
PyODDS is an endto end Python system for outlier detection with databas...
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Contextual Local Explanation for Black Box Classifiers
We introduce a new modelagnostic explanation technique which explains t...
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SubArchitecture Ensemble Pruning in Neural Architecture Search
Neural architecture search (NAS) is gaining more and more attention in r...
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Towards Generalizable Forgery Detection with Localityaware AutoEncoder
With advancements of deep learning techniques, it is now possible to gen...
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AutoGNN: Neural Architecture Search of Graph Neural Networks
Graph neural networks (GNN) has been successfully applied to operate on ...
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Fairness in Deep Learning: A Computational Perspective
Deep learning is increasingly being used in highstake decision making a...
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Learning Credible Deep Neural Networks with Rationale Regularization
Recent explainability related studies have shown that stateoftheart D...
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Deep Structured CrossModal Anomaly Detection
Anomaly detection is a fundamental problem in data mining field with man...
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Techniques for Automated Machine Learning
Automated machine learning (AutoML) aims to find optimal machine learnin...
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Evaluating Explanation Without Ground Truth in Interpretable Machine Learning
Interpretable Machine Learning (IML) has become increasingly important i...
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XFake: Explainable Fake News Detector with Visualizations
In this demo paper, we present the XFake system, an explainable fake new...
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Experience Replay Optimization
Experience replay enables reinforcement learning agents to memorize and ...
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Exact and Consistent Interpretation of Piecewise Linear Models Hidden behind APIs: A Closed Form Solution
More and more AI services are provided through APIs on cloud where predi...
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Coupled Variational Recurrent Collaborative Filtering
We focus on the problem of streaming recommender system and explore nove...
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Xia Hu
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