Search Results for author: Dejing Dou

Found 88 papers, 31 papers with code

Scalable Differential Privacy with Certified Robustness in Adversarial Learning

1 code implementation ICML 2020 Hai Phan, My T. Thai, Han Hu, Ruoming Jin, Tong Sun, Dejing Dou

In this paper, we aim to develop a scalable algorithm to preserve differential privacy (DP) in adversarial learning for deep neural networks (DNNs), with certified robustness to adversarial examples.

Parameter-Efficient Domain Knowledge Integration from Multiple Sources for Biomedical Pre-trained Language Models

no code implementations Findings (EMNLP) 2021 Qiuhao Lu, Dejing Dou, Thien Huu Nguyen

These knowledge adapters are pre-trained for individual domain knowledge sources and integrated via an attention-based knowledge controller to enrich PLMs.

Self-Supervised Learning

Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory

no code implementations NeurIPS 2021 Zeru Zhang, Jiayin Jin, Zijie Zhang, Yang Zhou, Xin Zhao, Jiaxiang Ren, Ji Liu, Lingfei Wu, Ruoming Jin, Dejing Dou

Despite achieving remarkable efficiency, traditional network pruning techniques often follow manually-crafted heuristics to generate pruned sparse networks.

Network Pruning

Generalized DataWeighting via Class-Level Gradient Manipulation

1 code implementation NeurIPS 2021 Can Chen, Shuhao Zheng, Xi Chen, Erqun Dong, Xue (Steve) Liu, Hao liu, Dejing Dou

To be specific, GDW unrolls the loss gradient to class-level gradients by the chain rule and reweights the flow of each gradient separately.

Generalized Data Weighting via Class-level Gradient Manipulation

1 code implementation29 Oct 2021 Can Chen, Shuhao Zheng, Xi Chen, Erqun Dong, Xue Liu, Hao liu, Dejing Dou

To be specific, GDW unrolls the loss gradient to class-level gradients by the chain rule and reweights the flow of each gradient separately.

SenseMag: Enabling Low-Cost Traffic Monitoring using Non-invasive Magnetic Sensing

no code implementations24 Oct 2021 Kafeng Wang, Haoyi Xiong, Jie Zhang, Hongyang Chen, Dejing Dou, Cheng-Zhong Xu

Extensive experiment based on real-word field deployment (on the highways in Shenzhen, China) shows that SenseMag significantly outperforms the existing methods in both classification accuracy and the granularity of vehicle types (i. e., 7 types by SenseMag versus 4 types by the existing work in comparisons).

AgFlow: Fast Model Selection of Penalized PCA via Implicit Regularization Effects of Gradient Flow

no code implementations7 Oct 2021 Haiyan Jiang, Haoyi Xiong, Dongrui Wu, Ji Liu, Dejing Dou

Principal component analysis (PCA) has been widely used as an effective technique for feature extraction and dimension reduction.

Dimensionality Reduction Model Selection

Exploring the Common Principal Subspace of Deep Features in Neural Networks

no code implementations6 Oct 2021 Haoran Liu, Haoyi Xiong, Yaqing Wang, Haozhe An, Dongrui Wu, Dejing Dou

Specifically, we design a new metric $\mathcal{P}$-vector to represent the principal subspace of deep features learned in a DNN, and propose to measure angles between the principal subspaces using $\mathcal{P}$-vectors.

Image Reconstruction Self-Supervised Learning

GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction

no code implementations24 Sep 2021 Shuangli Li, Jingbo Zhou, Tong Xu, Dejing Dou, Hui Xiong

Though graph contrastive learning (GCL) methods have achieved extraordinary performance with insufficient labeled data, most focused on designing data augmentation schemes for general graphs.

Contrastive Learning Data Augmentation +2

Cross-Model Consensus of Explanations and Beyond for Image Classification Models: An Empirical Study

no code implementations2 Sep 2021 Xuhong LI, Haoyi Xiong, Siyu Huang, Shilei Ji, Dejing Dou

Existing interpretation algorithms have found that, even deep models make the same and right predictions on the same image, they might rely on different sets of input features for classification.

Image Classification Semantic Segmentation +1

Semi-Supervised Active Learning with Temporal Output Discrepancy

1 code implementation ICCV 2021 Siyu Huang, Tianyang Wang, Haoyi Xiong, Jun Huan, Dejing Dou

To lower the cost of data annotation, active learning has been proposed to interactively query an oracle to annotate a small proportion of informative samples in an unlabeled dataset.

Active Learning Image Classification +1

Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity

1 code implementation21 Jul 2021 Shuangli Li, Jingbo Zhou, Tong Xu, Liang Huang, Fan Wang, Haoyi Xiong, Weili Huang, Dejing Dou, Hui Xiong

To this end, we propose a structure-aware interactive graph neural network (SIGN) which consists of two components: polar-inspired graph attention layers (PGAL) and pairwise interactive pooling (PiPool).

Drug Discovery Graph Attention

MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal

no code implementations12 Jul 2021 Weijia Zhang, Hao liu, Lijun Zha, HengShu Zhu, Ji Liu, Dejing Dou, Hui Xiong

Real estate appraisal refers to the process of developing an unbiased opinion for real property's market value, which plays a vital role in decision-making for various players in the marketplace (e. g., real estate agents, appraisers, lenders, and buyers).

Decision Making Graph Representation Learning +1

From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques

no code implementations2 Jul 2021 Zhiyuan Wang, Haoyi Xiong, Jie Zhang, Sijia Yang, Mehdi Boukhechba, Laura E. Barnes, Daqing Zhang, Dejing Dou

Mobile Sensing Apps have been widely used as a practical approach to collect behavioral and health-related information from individuals and provide timely intervention to promote health and well-beings, such as mental health and chronic cares.

Feature Grouping and Sparse Principal Component Analysis

1 code implementation25 Jun 2021 Haiyan Jiang, Shanshan Qin, Dejing Dou

In this paper, we propose a novel method called Feature Grouping and Sparse Principal Component Analysis (FGSPCA) which allows the loadings to belong to disjoint homogeneous groups, with sparsity as a special case.

Dimensionality Reduction

Robust Matrix Factorization with Grouping Effect

1 code implementation25 Jun 2021 Haiyan Jiang, Shuyu Li, Luwei Zhang, Haoyi Xiong, Dejing Dou

Compared with existing algorithms, the proposed GRMF can automatically learn the grouping structure and sparsity in MF without prior knowledge, by introducing a naturally adjustable non-convex regularization to achieve simultaneous sparsity and grouping effect.


Practical Assessment of Generalization Performance Robustness for Deep Networks via Contrastive Examples

no code implementations20 Jun 2021 Xuanyu Wu, Xuhong LI, Haoyi Xiong, Xiao Zhang, Siyu Huang, Dejing Dou

Incorporating with a set of randomized strategies for well-designed data transformations over the training set, ContRE adopts classification errors and Fisher ratios on the generated contrastive examples to assess and analyze the generalization performance of deep models in complement with a testing set.

Contrastive Learning

JIZHI: A Fast and Cost-Effective Model-As-A-Service System for Web-Scale Online Inference at Baidu

1 code implementation3 Jun 2021 Hao liu, Qian Gao, Jiang Li, Xiaochao Liao, Hao Xiong, Guangxing Chen, Wenlin Wang, Guobao Yang, Zhiwei Zha, daxiang dong, Dejing Dou, Haoyi Xiong

In this work, we present JIZHI - a Model-as-a-Service system - that per second handles hundreds of millions of online inference requests to huge deep models with more than trillions of sparse parameters, for over twenty real-time recommendation services at Baidu, Inc.

Recommendation Systems

From Distributed Machine Learning to Federated Learning: A Survey

no code implementations29 Apr 2021 Ji Liu, Jizhou Huang, Yang Zhou, Xuhong LI, Shilei Ji, Haoyi Xiong, Dejing Dou

Because of laws or regulations, the distributed data and computing resources cannot be directly shared among different regions or organizations for machine learning tasks.

Federated Learning

Cross-lingual Entity Alignment with Adversarial Kernel Embedding and Adversarial Knowledge Translation

1 code implementation16 Apr 2021 Gong Zhang, Yang Zhou, Sixing Wu, Zeru Zhang, Dejing Dou

With the guidance of known aligned entities in the context of multiple random walks, an adversarial knowledge translation model is developed to fill and translate masked entities in pairwise random walks from two KGs.

Entity Alignment Knowledge Graphs +1

Semantic Oppositeness Assisted Deep Contextual Modeling for Automatic Rumor Detection in Social Networks

no code implementations EACL 2021 Nisansa de Silva, Dejing Dou

Social networks face a major challenge in the form of rumors and fake news, due to their intrinsic nature of connecting users to millions of others, and of giving any individual the power to post anything.

Semantic Similarity Semantic Textual Similarity

ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows

1 code implementation CVPR 2021 Jie An, Siyu Huang, Yibing Song, Dejing Dou, Wei Liu, Jiebo Luo

The forward inference projects input images into deep features, while the backward inference remaps deep features back to input images in a lossless and unbiased way.

Style Transfer

SMILE: Self-Distilled MIxup for Efficient Transfer LEarning

no code implementations25 Mar 2021 Xingjian Li, Haoyi Xiong, Chengzhong Xu, Dejing Dou

Performing mixup for transfer learning with pre-trained models however is not that simple, a high capacity pre-trained model with a large fully-connected (FC) layer could easily overfit to the target dataset even with samples-to-labels mixed up.

Transfer Learning

Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond

no code implementations19 Mar 2021 Xuhong LI, Haoyi Xiong, Xingjian Li, Xuanyu Wu, Xiao Zhang, Ji Liu, Jiang Bian, Dejing Dou

Then, to understand the results of interpretation, we also survey the performance metrics for evaluating interpretation algorithms.

Adversarial Robustness

Adaptive Consistency Regularization for Semi-Supervised Transfer Learning

1 code implementation CVPR 2021 Abulikemu Abuduweili, Xingjian Li, Humphrey Shi, Cheng-Zhong Xu, Dejing Dou

To better exploit the value of both pre-trained weights and unlabeled target examples, we introduce adaptive consistency regularization that consists of two complementary components: Adaptive Knowledge Consistency (AKC) on the examples between the source and target model, and Adaptive Representation Consistency (ARC) on the target model between labeled and unlabeled examples.

Transfer Learning

Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learning

1 code implementation15 Feb 2021 Weijia Zhang, Hao liu, Fan Wang, Tong Xu, Haoran Xin, Dejing Dou, Hui Xiong

Electric Vehicle (EV) has become a preferable choice in the modern transportation system due to its environmental and energy sustainability.

Multi-agent Reinforcement Learning

Out-of-Town Recommendation with Travel Intention Modeling

1 code implementation29 Jan 2021 Haoran Xin, Xinjiang Lu, Tong Xu, Hao liu, Jingjing Gu, Dejing Dou, Hui Xiong

Second, a user-specific travel intention is formulated as an aggregation combining home-town preference and generic travel intention together, where the generic travel intention is regarded as a mixture of inherent intentions that can be learned by Neural Topic Model (NTM).

Empirical Studies on the Convergence of Feature Spaces in Deep Learning

no code implementations1 Jan 2021 Haoran Liu, Haoyi Xiong, Yaqing Wang, Haozhe An, Dongrui Wu, Dejing Dou

While deep learning is effective to learn features/representations from data, the distributions of samples in feature spaces learned by various architectures for different training tasks (e. g., latent layers of AEs and feature vectors in CNN classifiers) have not been well-studied or compared.

Image Reconstruction Self-Supervised Learning

Model information as an analysis tool in deep learning

no code implementations1 Jan 2021 Xiao Zhang, Di Hu, Xingjian Li, Dejing Dou, Ji Wu

We demonstrate using model information as a general analysis tool to gain insight into problems that arise in deep learning.

Information distance for neural network functions

no code implementations1 Jan 2021 Xiao Zhang, Dejing Dou, Ji Wu

We provide a practical distance measure in the space of functions parameterized by neural networks.

Democratizing Evaluation of Deep Model Interpretability through Consensus

no code implementations1 Jan 2021 Xuhong LI, Haoyi Xiong, Siyu Huang, Shilei Ji, Yanjie Fu, Dejing Dou

Given any task/dataset, Consensus first obtains the interpretation results using existing tools, e. g., LIME (Ribeiro et al., 2016), for every model in the committee, then aggregates the results from the entire committee and approximates the “ground truth” of interpretations through voting.

Feature Importance

Implicit Regularization Effects of Unbiased Random Label Noises with SGD

no code implementations1 Jan 2021 Haoyi Xiong, Xuhong LI, Boyang Yu, Dejing Dou, Dongrui Wu, Zhanxing Zhu

Random label noises (or observational noises) widely exist in practical machinelearning settings.

Can We Use Gradient Norm as a Measure of Generalization Error for Model Selection in Practice?

no code implementations1 Jan 2021 Haozhe An, Haoyi Xiong, Xuhong LI, Xingjian Li, Dejing Dou, Zhanxing Zhu

The recent theoretical investigation (Li et al., 2020) on the upper bound of generalization error of deep neural networks (DNNs) demonstrates the potential of using the gradient norm as a measure that complements validation accuracy for model selection in practice.

Model Selection

Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks

no code implementations30 Dec 2020 Jindong Han, Hao liu, HengShu Zhu, Hui Xiong, Dejing Dou

Specifically, we first propose a heterogeneous recurrent graph neural network to model the spatiotemporal autocorrelation among air quality and weather monitoring stations.

Graph Learning Multi-Task Learning

C-Watcher: A Framework for Early Detection of High-Risk Neighborhoods Ahead of COVID-19 Outbreak

no code implementations22 Dec 2020 Congxi Xiao, Jingbo Zhou, Jizhou Huang, An Zhuo, Ji Liu, Haoyi Xiong, Dejing Dou

Furthermore, to transfer the firsthand knowledge (witted in epicenters) to the target city before local outbreaks, we adopt a novel adversarial encoder framework to learn "city-invariant" representations from the mobility-related features for precise early detection of high-risk neighborhoods, even before any confirmed cases known, in the target city.

Distance-aware Molecule Graph Attention Network for Drug-Target Binding Affinity Prediction

1 code implementation17 Dec 2020 Jingbo Zhou, Shuangli Li, Liang Huang, Haoyi Xiong, Fan Wang, Tong Xu, Hui Xiong, Dejing Dou

The hierarchical attentive aggregation can capture spatial dependencies among atoms, as well as fuse the position-enhanced information with the capability of discriminating multiple spatial relations among atoms.

Drug Discovery Graph Attention +1

Temporal Relational Modeling with Self-Supervision for Action Segmentation

1 code implementation14 Dec 2020 Dong Wang, Di Hu, Xingjian Li, Dejing Dou

The main reason is that large number of nodes (i. e., video frames) makes GCNs hard to capture and model temporal relations in videos.

Action Recognition Action Segmentation +1

Adversarial Attacks on Deep Graph Matching

no code implementations NeurIPS 2020 Zijie Zhang, Zeru Zhang, Yang Zhou, Yelong Shen, Ruoming Jin, Dejing Dou

Despite achieving remarkable performance, deep graph learning models, such as node classification and network embedding, suffer from harassment caused by small adversarial perturbations.

Adversarial Attack Density Estimation +5

Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation

no code implementations Findings of the Association for Computational Linguistics 2020 Amir Pouran Ben Veyseh, Nasim Nour, Franck Dernoncourt, Quan Hung Tran, Dejing Dou, Thien Huu Nguyen

In addition, we propose a mechanism to obtain the importance scores for each word in the sentences based on the dependency trees that are then injected into the model to improve the representation vectors for ABSA.

Aspect-Based Sentiment Analysis

Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning

no code implementations EMNLP 2020 Amir Pouran Ben Veyseh, Nasim Nouri, Franck Dernoncourt, Dejing Dou, Thien Huu Nguyen

In this work, we propose to incorporate the syntactic structures of the sentences into the deep learning models for TOWE, leveraging the syntax-based opinion possibility scores and the syntactic connections between the words.

Aspect-oriented Opinion Extraction

Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement

no code implementations16 Oct 2020 Xingjian Li, Di Hu, Xuhong LI, Haoyi Xiong, Zhi Ye, Zhipeng Wang, Chengzhong Xu, Dejing Dou

Fine-tuning deep neural networks pre-trained on large scale datasets is one of the most practical transfer learning paradigm given limited quantity of training samples.

Transfer Learning

Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching

1 code implementation NeurIPS 2020 Di Hu, Rui Qian, Minyue Jiang, Xiao Tan, Shilei Wen, Errui Ding, Weiyao Lin, Dejing Dou

First, we propose to learn robust object representations by aggregating the candidate sound localization results in the single source scenes.

Object Localization

Measuring Information Transfer in Neural Networks

no code implementations16 Sep 2020 Xiao Zhang, Xingjian Li, Dejing Dou, Ji Wu

We propose a practical measure of the generalizable information in a neural network model based on prequential coding, which we term Information Transfer ($L_{IT}$).

Continual Learning Transfer Learning

XMixup: Efficient Transfer Learning with Auxiliary Samples by Cross-domain Mixup

no code implementations20 Jul 2020 Xingjian Li, Haoyi Xiong, Haozhe An, Cheng-Zhong Xu, Dejing Dou

While the existing multitask learning algorithms need to run backpropagation over both the source and target datasets and usually consume a higher gradient complexity, XMixup transfers the knowledge from source to target tasks more efficiently: for every class of the target task, XMixup selects the auxiliary samples from the source dataset and augments training samples via the simple mixup strategy.

Transfer Learning

Generating Person Images with Appearance-aware Pose Stylizer

1 code implementation17 Jul 2020 Siyu Huang, Haoyi Xiong, Zhi-Qi Cheng, Qingzhong Wang, Xingran Zhou, Bihan Wen, Jun Huan, Dejing Dou

Generation of high-quality person images is challenging, due to the sophisticated entanglements among image factors, e. g., appearance, pose, foreground, background, local details, global structures, etc.

Image Generation

Representation Transfer by Optimal Transport

no code implementations13 Jul 2020 Xuhong Li, Yves GRANDVALET, Rémi Flamary, Nicolas Courty, Dejing Dou

We use optimal transport to quantify the match between two representations, yielding a distance that embeds some invariances inherent to the representation of deep networks.

Knowledge Distillation Model Compression +1

RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr

1 code implementation ICML 2020 Xingjian Li, Haoyi Xiong, Haozhe An, Cheng-Zhong Xu, Dejing Dou

RIFLE brings meaningful updates to the weights of deep CNN layers and improves low-level feature learning, while the effects of randomization can be easily converged throughout the overall learning procedure.

Transfer Learning

Exploiting the Syntax-Model Consistency for Neural Relation Extraction

no code implementations ACL 2020 Amir Pouran Ben Veyseh, Franck Dernoncourt, Dejing Dou, Thien Huu Nguyen

In order to overcome these issues, we propose a novel deep learning model for RE that uses the dependency trees to extract the syntax-based importance scores for the words, serving as a tree representation to introduce syntactic information into the models with greater generalization.

Multi-Task Learning Relation Extraction

Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition

1 code implementation ECCV 2020 Di Hu, Xuhong LI, Lichao Mou, Pu Jin, Dong Chen, Liping Jing, Xiaoxiang Zhu, Dejing Dou

With the help of this dataset, we evaluate three proposed approaches for transferring the sound event knowledge to the aerial scene recognition task in a multimodal learning framework, and show the benefit of exploiting the audio information for the aerial scene recognition.

Scene Recognition

Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions

1 code implementation14 May 2020 Di Hu, Lichao Mou, Qingzhong Wang, Junyu. Gao, Yuansheng Hua, Dejing Dou, Xiao Xiang Zhu

Visual crowd counting has been recently studied as a way to enable people counting in crowd scenes from images.

Affine Transformation Crowd Counting

Quantifying the Economic Impact of COVID-19 in Mainland China Using Human Mobility Data

no code implementations6 May 2020 Jizhou Huang, Haifeng Wang, Haoyi Xiong, Miao Fan, An Zhuo, Ying Li, Dejing Dou

While these strategies have effectively dealt with the critical situations of outbreaks, the combination of the pandemic and mobility controls has slowed China's economic growth, resulting in the first quarterly decline of Gross Domestic Product (GDP) since GDP began to be calculated, in 1992.

Pay Attention to Features, Transfer Learn Faster CNNs

no code implementations ICLR 2020 Kafeng Wang, Xitong Gao, Yiren Zhao, Xingjian Li, Dejing Dou, Cheng-Zhong Xu

Deep convolutional neural networks are now widely deployed in vision applications, but a limited size of training data can restrict their task performance.

Transfer Learning

COLAM: Co-Learning of Deep Neural Networks and Soft Labels via Alternating Minimization

no code implementations26 Apr 2020 Xingjian Li, Haoyi Xiong, Haozhe An, Dejing Dou, Chengzhong Xu

Softening labels of training datasets with respect to data representations has been frequently used to improve the training of deep neural networks (DNNs).

General Classification

Ontology-based Interpretable Machine Learning for Textual Data

2 code implementations1 Apr 2020 Phung Lai, NhatHai Phan, Han Hu, Anuja Badeti, David Newman, Dejing Dou

In this paper, we introduce a novel interpreting framework that learns an interpretable model based on an ontology-based sampling technique to explain agnostic prediction models.

Interpretable Machine Learning

Parameter-Free Style Projection for Arbitrary Style Transfer

no code implementations17 Mar 2020 Siyu Huang, Haoyi Xiong, Tianyang Wang, Qingzhong Wang, Zeyu Chen, Jun Huan, Dejing Dou

Arbitrary image style transfer is a challenging task which aims to stylize a content image conditioned on an arbitrary style image.

Style Transfer

Label-guided Learning for Text Classification

no code implementations25 Feb 2020 Xien Liu, Song Wang, Xiao Zhang, Xinxin You, Ji Wu, Dejing Dou

In this study, we propose a label-guided learning framework LguidedLearn for text representation and classification.

General Classification Representation Learning +1

Curriculum Audiovisual Learning

no code implementations26 Jan 2020 Di Hu, Zheng Wang, Haoyi Xiong, Dong Wang, Feiping Nie, Dejing Dou

Associating sound and its producer in complex audiovisual scene is a challenging task, especially when we are lack of annotated training data.

Curriculum Learning

An Empirical Study on the Relation between Network Interpretability and Adversarial Robustness

1 code implementation7 Dec 2019 Adam Noack, Isaac Ahern, Dejing Dou, Boyang Li

We demonstrate that training the networks to have interpretable gradients improves their robustness to adversarial perturbations.

Adversarial Robustness Image Classification

A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistency

1 code implementation5 Nov 2019 Amir Pouran Ben Veyseh, Franck Dernoncourt, Dejing Dou, Thien Huu Nguyen

In this work, we propose a novel model for DE that simultaneously performs the two tasks in a single framework to benefit from their inter-dependencies.

Definition Extraction Multi-Task Learning +1

Differential Privacy in Adversarial Learning with Provable Robustness

no code implementations25 Sep 2019 NhatHai Phan, My T. Thai, Ruoming Jin, Han Hu, Dejing Dou

In this paper, we aim to develop a novel mechanism to preserve differential privacy (DP) in adversarial learning for deep neural networks, with provable robustness to adversarial examples.

Language-independent Cross-lingual Contextual Representations

no code implementations25 Sep 2019 Xiao Zhang, Song Wang, Dejing Dou, Xien Liu, Thien Huu Nguyen, Ji Wu

Contextual representation models like BERT have achieved state-of-the-art performance on a diverse range of NLP tasks.

Transfer Learning Zero-Shot Cross-Lingual Transfer

NormLime: A New Feature Importance Metric for Explaining Deep Neural Networks

no code implementations ICLR 2020 Isaac Ahern, Adam Noack, Luis Guzman-Nateras, Dejing Dou, Boyang Li, Jun Huan

The problem of explaining deep learning models, and model predictions generally, has attracted intensive interest recently.

Feature Importance

Improving Adversarial Robustness via Attention and Adversarial Logit Pairing

no code implementations23 Aug 2019 Dou Goodman, Xingjian Li, Ji Liu, Dejing Dou, Tao Wei

Finally, we conduct extensive experiments using a wide range of datasets and the experiment results show that our AT+ALP achieves the state of the art defense performance.

Adversarial Robustness

Learning Conceptual-Contextual Embeddings for Medical Text

no code implementations16 Aug 2019 Xiao Zhang, Dejing Dou, Ji Wu

External knowledge is often useful for natural language understanding tasks.

Natural Language Understanding

Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures

1 code implementation ACL 2019 Amir Pouran Ben Veyseh, Thien Huu Nguyen, Dejing Dou

In this work, we introduce a novel graph-based neural network for EFP that can integrate the semantic and syntactic information more effectively.

Improving Cross-Domain Performance for Relation Extraction via Dependency Prediction and Information Flow Control

no code implementations7 Jul 2019 Amir Pouran Ben Veyseh, Thien Huu Nguyen, Dejing Dou

The current deep learning models for relation extraction has mainly exploited this dependency information by guiding their computation along the structures of the dependency trees.

Domain Generalization Relation Extraction

Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness

4 code implementations2 Jun 2019 NhatHai Phan, Minh Vu, Yang Liu, Ruoming Jin, Dejing Dou, Xintao Wu, My T. Thai

In this paper, we propose a novel Heterogeneous Gaussian Mechanism (HGM) to preserve differential privacy in deep neural networks, with provable robustness against adversarial examples.

Preserving Differential Privacy in Adversarial Learning with Provable Robustness

no code implementations23 Mar 2019 NhatHai Phan, My T. Thai, Ruoming Jin, Han Hu, Dejing Dou

In this paper, we aim to develop a novel mechanism to preserve differential privacy (DP) in adversarial learning for deep neural networks, with provable robustness to adversarial examples.

Cryptography and Security

Logic Rules Powered Knowledge Graph Embedding

no code implementations9 Mar 2019 Pengwei Wang, Dejing Dou, Fangzhao Wu, Nisansa de Silva, Lianwen Jin

And then, to put both triples and mined logic rules within the same semantic space, all triples in the knowledge graph are represented as first-order logic.

Knowledge Graph Embedding Link Prediction +1

Delta Embedding Learning

no code implementations ACL 2019 Xiao Zhang, Ji Wu, Dejing Dou

Evaluation also confirms the tuned word embeddings have better semantic properties.

Reading Comprehension Word Embeddings

On Adversarial Examples for Character-Level Neural Machine Translation

3 code implementations COLING 2018 Javid Ebrahimi, Daniel Lowd, Dejing Dou

Evaluating on adversarial examples has become a standard procedure to measure robustness of deep learning models.

Machine Translation Translation

HotFlip: White-Box Adversarial Examples for Text Classification

2 code implementations ACL 2018 Javid Ebrahimi, Anyi Rao, Daniel Lowd, Dejing Dou

We propose an efficient method to generate white-box adversarial examples to trick a character-level neural classifier.

General Classification Text Classification

Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning

2 code implementations18 Sep 2017 NhatHai Phan, Xintao Wu, Han Hu, Dejing Dou

In this paper, we focus on developing a novel mechanism to preserve differential privacy in deep neural networks, such that: (1) The privacy budget consumption is totally independent of the number of training steps; (2) It has the ability to adaptively inject noise into features based on the contribution of each to the output; and (3) It could be applied in a variety of different deep neural networks.

Preserving Differential Privacy in Convolutional Deep Belief Networks

2 code implementations25 Jun 2017 NhatHai Phan, Xintao Wu, Dejing Dou

However, only a few scientific studies on preserving privacy in deep learning have been conducted.

A Probabilistic Approach to Knowledge Translation

no code implementations12 Jul 2015 Shangpu Jiang, Daniel Lowd, Dejing Dou

In this paper, we focus on a novel knowledge reuse scenario where the knowledge in the source schema needs to be translated to a semantically heterogeneous target schema.

Transfer Learning Translation

Ontology Matching with Knowledge Rules

no code implementations11 Jul 2015 Shangpu Jiang, Daniel Lowd, Dejing Dou

We use a probabilistic framework to integrate this new knowledge-based strategy with standard terminology-based and structure-based strategies.

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