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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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Sparse-Interest 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 Procedurally-Generated Environments
Exploration under sparse reward is a long-standing challenge of model-fr...
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Generative Counterfactuals for Neural Networks via Attribute-Informed 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 Post-Hoc 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 Click-Through Rate Prediction
Click-Through 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 ever-changi...
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AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation 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 high-stake 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 Model-Level 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 End-to-end 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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Multi-Channel 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 open-source Python package developed to interpret deep model...
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RLCard: A Toolkit for Reinforcement Learning in Card Games
RLCard is an open-source toolkit for reinforcement learning research in ...
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PyODDS: An End-to-End Outlier Detection System
PyODDS is an end-to end Python system for outlier detection with databas...
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Contextual Local Explanation for Black Box Classifiers
We introduce a new model-agnostic explanation technique which explains t...
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Sub-Architecture 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 Locality-aware AutoEncoder
With advancements of deep learning techniques, it is now possible to gen...
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Auto-GNN: 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 high-stake decision making a...
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Learning Credible Deep Neural Networks with Rationale Regularization
Recent explainability related studies have shown that state-of-the-art D...
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Deep Structured Cross-Modal 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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Deep Bayesian Optimization on Attributed Graphs
Attributed graphs, which contain rich contextual features beyond just ne...
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Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding
Networks have been widely used as the data structure for abstracting rea...
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Deep Representation Learning for Social Network Analysis
Social network analysis is an important problem in data mining. A fundam...
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Open Issues in Combating Fake News: Interpretability as an Opportunity
Combating fake news needs a variety of defense methods. Although rumor d...
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On Attribution of Recurrent Neural Network Predictions via Additive Decomposition
RNN models have achieved the state-of-the-art performance in a wide rang...
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Multi-Label Adversarial Perturbations
Adversarial examples are delicately perturbed inputs, which aim to misle...
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Techniques for Interpretable Machine Learning
Interpretable machine learning tackles the important problem that humans...
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Efficient Neural Architecture Search with Network Morphism
While neural architecture search (NAS) has drawn increasing attention fo...
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r-instance Learning for Missing People Tweets Identification
The number of missing people (i.e., people who get lost) greatly increas...
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Understanding and Monitoring Human Trafficking via Social Sensors: A Sociological Approach
Human trafficking is a serious social problem, and it is challenging mai...
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Towards Explanation of DNN-based Prediction with Guided Feature Inversion
While deep neural networks (DNN) have become an effective computational ...
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