b'Xia Hu'

Dirichlet Energy Constrained Learning for Deep Graph Neural Networks

Graph neural networks (GNNs) integrate deep architectures and topologica...
07/06/2021 ∙ by Kaixiong Zhou, et al. ∙ 0 ∙ share

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Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning

Recent work on graph generative models has made remarkable progress towa...
06/29/2021 ∙ by Kiarash Zahirnia, et al. ∙ 0 ∙ share

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Fairness via Representation Neutralization

Existing bias mitigation methods for DNN models primarily work on learni...
06/23/2021 ∙ by Mengnan Du, et al. ∙ 5 ∙ share

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Model-Based Counterfactual Synthesizer for Interpretation

Counterfactuals, serving as one of the emerging type of model interpreta...
06/16/2021 ∙ by Fan Yang, et al. ∙ 12 ∙ share

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DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning

Games are abstractions of the real world, where artificial agents learn ...
06/11/2021 ∙ by Daochen Zha, et al. ∙ 0 ∙ share

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A General Taylor Framework for Unifying and Revisiting Attribution Methods

Attribution methods provide an insight into the decision-making process ...
05/28/2021 ∙ by Huiqi Deng, et al. ∙ 0 ∙ share

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Learning Disentangled Representations for Time Series

Time-series representation learning is a fundamental task for time-serie...
05/17/2021 ∙ by Yuening Li, et al. ∙ 17 ∙ share

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Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution

Back propagation based visualizations have been proposed to interpret de...
04/14/2021 ∙ by Huiqi Deng, et al. ∙ 0 ∙ share

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DivAug: Plug-in Automated Data Augmentation with Explicit Diversity Maximization

Human-designed data augmentation strategies have been replaced by automa...
03/26/2021 ∙ by Zirui Liu, et al. ∙ 0 ∙ share

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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...
03/11/2021 ∙ by Mengnan Du, et al. ∙ 0 ∙ share

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Model Complexity of Deep Learning: A Survey

Model complexity is a fundamental problem in deep learning. In this pape...
03/08/2021 ∙ by Xia Hu, et al. ∙ 0 ∙ share

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Dynamic Memory based Attention Network for Sequential Recommendation

Sequential recommendation has become increasingly essential in various o...
02/18/2021 ∙ by Qiaoyu Tan, et al. ∙ 0 ∙ share

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Sparse-Interest Network for Sequential Recommendation

Recent methods in sequential recommendation focus on learning an overall...
02/18/2021 ∙ by Qiaoyu Tan, et al. ∙ 0 ∙ share

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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...
01/20/2021 ∙ by Daochen Zha, et al. ∙ 6 ∙ share

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Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation

With the wide use of deep neural networks (DNN), model interpretability ...
01/18/2021 ∙ by Fan Yang, et al. ∙ 0 ∙ share

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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...
11/17/2020 ∙ by Ruixiang Tang, et al. ∙ 4 ∙ share

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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...
10/29/2020 ∙ by Imtiaz Ahmed, et al. ∙ 0 ∙ share

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Towards Interaction Detection Using Topological Analysis on Neural Networks

Detecting statistical interactions between input features is a crucial a...
10/25/2020 ∙ by Zirui Liu, et al. ∙ 0 ∙ share

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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...
09/16/2020 ∙ by Ninghao Liu, et al. ∙ 0 ∙ share

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A Unified Taylor Framework for Revisiting Attribution Methods

Attribution methods have been developed to understand the decision makin...
08/21/2020 ∙ by Huiqi Deng, et al. ∙ 0 ∙ share

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Explainable Recommender Systems via Resolving Learning Representations

Recommender systems play a fundamental role in web applications in filte...
08/21/2020 ∙ by Ninghao Liu, et al. ∙ 13 ∙ share

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Machine Learning Explanations to Prevent Overtrust in Fake News Detection

Combating fake news and misinformation propagation is a challenging task...
07/24/2020 ∙ by Sina Mohseni, et al. ∙ 16 ∙ share

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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...
06/29/2020 ∙ by Qingquan Song, et al. ∙ 0 ∙ share

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AutoRec: An Automated Recommender System

Realistic recommender systems are often required to adapt to ever-changi...
06/26/2020 ∙ by Ting-Hsiang Wang, et al. ∙ 0 ∙ share

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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...
06/19/2020 ∙ by Yuening Li, et al. ∙ 7 ∙ share

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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...
06/16/2020 ∙ by Xia Hu, et al. ∙ 0 ∙ share

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Mitigating Gender Bias in Captioning Systems

Image captioning has made substantial progress with huge supporting imag...
06/15/2020 ∙ by Ruixiang Tang, et al. ∙ 0 ∙ share

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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...
06/15/2020 ∙ by Ruixiang Tang, et al. ∙ 0 ∙ share

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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...
06/12/2020 ∙ by Kaixiong Zhou, et al. ∙ 0 ∙ share

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XGNN: Towards Model-Level Explanations of Graph Neural Networks

Graphs neural networks (GNNs) learn node features by aggregating and com...
06/03/2020 ∙ by Hao Yuan, et al. ∙ 39 ∙ share

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Adversarial Machine Learning: An Interpretation Perspective

Recent years have witnessed the significant advances of machine learning...
04/23/2020 ∙ by Ninghao Liu, et al. ∙ 0 ∙ share

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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...
03/12/2020 ∙ by Yuening Li, et al. ∙ 0 ∙ share

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Learning to Hash with Graph Neural Networks for Recommender Systems

Graph representation learning has attracted much attention in supporting...
03/04/2020 ∙ by Qiaoyu Tan, et al. ∙ 0 ∙ share

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Multi-Channel Graph Convolutional Networks

Graph neural networks (GNN) has been demonstrated to be effective in cla...
12/17/2019 ∙ by Kaixiong Zhou, et al. ∙ 0 ∙ share

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XDeep: An Interpretation Tool for Deep Neural Networks

XDeep is an open-source Python package developed to interpret deep model...
11/04/2019 ∙ by Fan Yang, et al. ∙ 0 ∙ share

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RLCard: A Toolkit for Reinforcement Learning in Card Games

RLCard is an open-source toolkit for reinforcement learning research in ...
10/10/2019 ∙ by Daochen Zha, et al. ∙ 2 ∙ share

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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...
10/07/2019 ∙ by Yuening Li, et al. ∙ 41 ∙ share

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Contextual Local Explanation for Black Box Classifiers

We introduce a new model-agnostic explanation technique which explains t...
10/02/2019 ∙ by Zijian Zhang, et al. ∙ 0 ∙ share

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Sub-Architecture Ensemble Pruning in Neural Architecture Search

Neural architecture search (NAS) is gaining more and more attention in r...
10/01/2019 ∙ by Yijun Bian, et al. ∙ 0 ∙ share

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Towards Generalizable Forgery Detection with Locality-aware AutoEncoder

With advancements of deep learning techniques, it is now possible to gen...
09/13/2019 ∙ by Mengnan Du, et al. ∙ 0 ∙ share

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Auto-GNN: Neural Architecture Search of Graph Neural Networks

Graph neural networks (GNN) has been successfully applied to operate on ...
09/07/2019 ∙ by Kaixiong Zhou, et al. ∙ 0 ∙ share

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Fairness in Deep Learning: A Computational Perspective

Deep learning is increasingly being used in high-stake decision making a...
08/23/2019 ∙ by Mengnan Du, et al. ∙ 0 ∙ share

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Learning Credible Deep Neural Networks with Rationale Regularization

Recent explainability related studies have shown that state-of-the-art D...
08/13/2019 ∙ by Mengnan Du, et al. ∙ 0 ∙ share

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Deep Structured Cross-Modal Anomaly Detection

Anomaly detection is a fundamental problem in data mining field with man...
08/11/2019 ∙ by Yuening Li, et al. ∙ 8 ∙ share

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Techniques for Automated Machine Learning

Automated machine learning (AutoML) aims to find optimal machine learnin...
07/21/2019 ∙ by Yi-Wei Chen, et al. ∙ 11 ∙ share

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Evaluating Explanation Without Ground Truth in Interpretable Machine Learning

Interpretable Machine Learning (IML) has become increasingly important i...
07/16/2019 ∙ by Fan Yang, et al. ∙ 7 ∙ share

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XFake: Explainable Fake News Detector with Visualizations

In this demo paper, we present the XFake system, an explainable fake new...
07/08/2019 ∙ by Fan Yang, et al. ∙ 0 ∙ share

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Experience Replay Optimization

Experience replay enables reinforcement learning agents to memorize and ...
06/19/2019 ∙ by Daochen Zha, et al. ∙ 0 ∙ share

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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...
06/17/2019 ∙ by Zicun Cong, et al. ∙ 2 ∙ share

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Coupled Variational Recurrent Collaborative Filtering

We focus on the problem of streaming recommender system and explore nove...
06/11/2019 ∙ by Qingquan Song, et al. ∙ 0 ∙ share

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Xia Hu

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