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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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A Unified Taylor Framework for Revisiting Attribution Methods
Attribution methods have been developed to understand the decision makin...
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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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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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Adversarial Machine Learning: An Interpretation Perspective
Recent years have witnessed the significant advances of machine learning...
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Score-CAM:Improved Visual Explanations Via Score-Weighted Class Activation Mapping
Recently, more and more attention has been drawn into the internal mecha...
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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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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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SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks
Anomaly detection aims to distinguish observations that are rare and dif...
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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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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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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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Techniques for Interpretable Machine Learning
Interpretable machine learning tackles the important problem that humans...
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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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