Search Results for author: Shuai Wang

Found 85 papers, 21 papers with code

Automatic recognition of abdominal lymph nodes from clinical text

1 code implementation EMNLP (ClinicalNLP) 2020 Yifan Peng, SungWon Lee, Daniel C. Elton, Thomas Shen, Yu-Xing Tang, Qingyu Chen, Shuai Wang, Yingying Zhu, Ronald Summers, Zhiyong Lu

We then introduce an end-to-end approach based on the combination of rules and transformer-based methods to detect these abdominal lymph node mentions and classify their types from the MRI radiology reports.

XInsight: eXplainable Data Analysis Through The Lens of Causality

no code implementations26 Jul 2022 Pingchuan Ma, Rui Ding, Shuai Wang, Shi Han, Dongmei Zhang

XInsight is a three-module, end-to-end pipeline designed to extract causal graphs, translate causal primitives into XDA semantics, and quantify the quantitative contribution of each explanation to a data fact.

Decision Making

Cross Vision-RF Gait Re-identification with Low-cost RGB-D Cameras and mmWave Radars

no code implementations16 Jul 2022 Dongjiang Cao, Ruofeng Liu, Hao Li, Shuai Wang, Wenchao Jiang, Chris Xiaoxuan Lu

Human identification is a key requirement for many applications in everyday life, such as personalized services, automatic surveillance, continuous authentication, and contact tracing during pandemics, etc.

Metric Learning Person Re-Identification

A Framework Based on Generational and Environmental Response Strategies for Dynamic Multi-objective Optimization

no code implementations6 Jul 2022 Qingya Li, Xiangzhi Liu, Fuqiang Wang, Shuai Wang, Peng Zhang, Xiaoming Wu

In this paper, a novel framework based on generational and environmental response strategies (FGERS) is proposed, in which response strategies are run both in the environmental change stage and the environmental static stage to obtain population evolution information of those both stages.

VIP-SLAM: An Efficient Tightly-Coupled RGB-D Visual Inertial Planar SLAM

no code implementations4 Jul 2022 Danpeng Chen, Shuai Wang, Weijian Xie, Shangjin Zhai, Nan Wang, Hujun Bao, Guofeng Zhang

Even if the plane parameters are involved in the optimization, we effectively simplify the back-end map by using planar structures.

Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification

1 code implementation3 Jun 2022 Shuai Wang, Chengyang Li, Qi Hao, Chengzhong Xu, Derrick Wing Kwan Ng, Yonina C. Eldar, H. Vincent Poor

However, it is challenging to determine the network resources and road sensor poses for multi-stage training with multi-modal datasets in multi-variant scenarios.

Federated Learning Management

Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees

1 code implementation24 May 2022 Banghua Zhu, Lun Wang, Qi Pang, Shuai Wang, Jiantao Jiao, Dawn Song, Michael I. Jordan

In contrast to prior work, our proposed protocols improve the dimension dependence and achieve a tight statistical rate in terms of all the parameters for strongly convex losses.

Federated Learning

To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers

no code implementations30 Apr 2022 Hang Li, Shuai Wang, Shengyao Zhuang, Ahmed Mourad, Xueguang Ma, Jimmy Lin, Guido Zuccon

In this paper we consider the problem of combining the relevance signals from sparse and dense retrievers in the context of Pseudo Relevance Feedback (PRF).

Information Retrieval Language Modelling

Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods

no code implementations28 Apr 2022 Zongze Li, Shuai Wang, Qingfeng Lin, Yang Li, Miaowen Wen, Yik-Chung Wu, H. Vincent Poor

Reconfigurable intelligent surfaces (RISs) have a revolutionary capability to customize the radio propagation environment for wireless networks.

Federated Stochastic Primal-dual Learning with Differential Privacy

no code implementations26 Apr 2022 Yiwei Li, Shuai Wang, Tsung-Hui Chang, Chong-Yung Chi

Specifically, we show that, by guaranteeing $(\epsilon, \delta)$-DP for each client per communication round, the proposed algorithm guarantees $(\mathcal{O}(q\epsilon \sqrt{p T}), \delta)$-DP after $T$ communication rounds while maintaining an $\mathcal{O}(1/\sqrt{pTQ})$ convergence rate for a convex and non-smooth learning problem, where $Q$ is the number of local SGD steps, $p$ is the client sampling probability, $q=\max_{i} q_i/\sqrt{1-q_i}$ and $q_i$ is the data sampling probability of each client under PCP.

Federated Learning

From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature Search

1 code implementation6 Apr 2022 Shuai Wang, Harrisen Scells, Justin Clark, Bevan Koopman, Guido Zuccon

However, we show pseudo seed studies are not representative of real seed studies used by information specialists.

Accelerating Federated Edge Learning via Topology Optimization

no code implementations1 Apr 2022 Shanfeng Huang, Zezhong Zhang, Shuai Wang, Rui Wang, Kaibin Huang

In this paper, a novel topology-optimized federated edge learning (TOFEL) scheme is proposed to tackle the heterogeneity issue in federated learning and to improve the communication-and-computation efficiency.

3D Object Detection Decision Making +4

SGDR: Semantic-guided Disentangled Representation for Unsupervised Cross-modality Medical Image Segmentation

no code implementations26 Mar 2022 Shuai Wang, Li Rui

Disentangled representation is a powerful technique to tackle domain shift problem in medical image analysis in unsupervised domain adaptation setting. However, previous methods only focus on exacting domain-invariant feature and ignore whether exacted feature is meaningful for downstream tasks. We propose a novel framework, called semantic-guided disentangled representation (SGDR), an effective method to exact semantically meaningful feature for segmentation task to improve performance of cross modality medical image segmentation in unsupervised domain adaptation setting. To exact the meaningful domain-invariant features of different modality, we introduce a content discriminator to force the content representation to be embedded to the same space and a feature discriminator to exact the meaningful representation. We also use pixel-level annotations to guide the encoder to learn features that are meaningful for segmentation task. We validated our method on two public datasets and experiment results show that our approach outperforms the state of the art methods on two evaluation metrics by a significant margin.

Medical Image Segmentation Semantic Segmentation +1

Source-free Domain Adaptation for Multi-site and Lifespan Brain Skull Stripping

no code implementations8 Mar 2022 Yunxiang Li, Ruilong Dan, Shuai Wang, Yifan Cao, Xiangde Luo, Chenghao Tan, Gangyong Jia, Huiyu Zhou, Yaqi Wang, Li Wang

In this paper, we design a source-free domain adaptation framework (SDAF) for multi-site and lifespan skull stripping that can accomplish domain adaptation without access to source domain images.

Domain Adaptation Skull Stripping

On the Security of LEO Satellite Communication Systems: Vulnerabilities, Countermeasures, and Future Trends

no code implementations9 Jan 2022 Pingyue Yue, Jianping An, Jiankang Zhang, Gaofeng Pan, Shuai Wang, Pei Xiao, Lajos Hanzo

Low Earth Orbit (LEO) satellite systems undergo a period of rapid development driven by the ever-increasing user demands, reduced costs, and technological progress.

Dominating Vertical Collaborative Learning Systems

no code implementations8 Jan 2022 Qi Pang, Yuanyuan Yuan, Shuai Wang

Vertical collaborative learning system also known as vertical federated learning (VFL) system has recently become prominent as a concept to process data distributed across many individual sources without the need to centralize it.

Federated Learning Privacy Preserving

Automated Side Channel Analysis of Media Software with Manifold Learning

1 code implementation9 Dec 2021 Yuanyuan Yuan, Qi Pang, Shuai Wang

Recent advances in representation learning and perceptual learning inspired us to consider the reconstruction of media inputs from side channel traces as a cross-modality manifold learning task that can be addressed in a unified manner with an autoencoder framework trained to learn the mapping between media inputs and side channel observations.

Representation Learning Side Channel Analysis

Seed-driven Document Ranking for Systematic Reviews: A Reproducibility Study

1 code implementation8 Dec 2021 Shuai Wang, Harrisen Scells, Ahmed Mourad, Guido Zuccon

Our results also indicate that our reproduced screening prioritisation method, (1) is generalisable across datasets of similar and different topicality compared to the original implementation, (2) that when using multiple seed studies, the effectiveness of the method increases using our techniques to enable this, (3) and that the use of multiple seed studies produces more stable rankings compared to single seed studies.

Document Ranking

MDPFuzz: Testing Models Solving Markov Decision Processes

no code implementations6 Dec 2021 Qi Pang, Yuanyuan Yuan, Shuai Wang

During fuzzing, MDPFuzzer decides which mutated state to retain by measuring if it can reduce cumulative rewards or form a new state sequence.

Autonomous Driving Decision Making +1

You Can't See the Forest for Its Trees: Assessing Deep Neural Network Testing via NeuraL Coverage

no code implementations3 Dec 2021 Yuanyuan Yuan, Qi Pang, Shuai Wang

This paper summarizes eight design requirements for DNN testing criteria, taking into account distribution properties and practical concerns.

DNN Testing

Dispensed Transformer Network for Unsupervised Domain Adaptation

no code implementations28 Oct 2021 Yunxiang Li, Jingxiong Li, Ruilong Dan, Shuai Wang, Kai Jin, Guodong Zeng, Jun Wang, Xiangji Pan, Qianni Zhang, Huiyu Zhou, Qun Jin, Li Wang, Yaqi Wang

To mitigate this problem, a novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper.

Unsupervised Domain Adaptation

Dual Shape Guided Segmentation Network for Organs-at-Risk in Head and Neck CT Images

no code implementations23 Oct 2021 Shuai Wang, Theodore Yanagihara, Bhishamjit Chera, Colette Shen, Pew-Thian Yap, Jun Lian

To deal with the large shape variation and unclear boundary of OARs in CT images, we represent the organ shape using an organ-specific unilateral inverse-distance map (UIDM) and guide the segmentation task from two different perspectives: direct shape guidance by following the segmentation prediction and across shape guidance by sharing the segmentation feature.

GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation

1 code implementation30 Sep 2021 Yunxiang Li, Shuai Wang, Jun Wang, Guodong Zeng, Wenjun Liu, Qianni Zhang, Qun Jin, Yaqi Wang

In this paper, we propose a novel end-to-end U-Net like Group Transformer Network (GT U-Net) for the tooth root segmentation.


FED-$\chi^2$: Secure Federated Correlation Test

no code implementations29 Sep 2021 Lun Wang, Qi Pang, Shuai Wang, Dawn Song

In this paper, we propose the first secure federated $\chi^2$-test protocol, FED-$\chi^2$.

Secure Byzantine-Robust Federated Learning with Dimension-free Error

no code implementations29 Sep 2021 Lun Wang, Qi Pang, Shuai Wang, Dawn Song

In the present work, we propose a federated learning protocol with bi-directional security guarantees.

Federated Learning

Paint4Poem: A Dataset for Artistic Visualization of Classical Chinese Poems

1 code implementation23 Sep 2021 Dan Li, Shuai Wang, Jie Zou, Chang Tian, Elisha Nieuwburg, Fengyuan Sun, Evangelos Kanoulas

We create abenchmark for Paint4Poem: we train two representative text-to-image generation models: AttnGAN and MirrorGAN, and evaluate theirperformance regarding painting pictorial quality, painting stylistic relevance, and semantic relevance between poems and paintings. The results indicate that the models are able to generate paintings that have good pictorial quality and mimic Feng Zikai's style, but thereflection of poem semantics is limited.

Few-Shot Learning Text to image generation +1

Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimization

no code implementations31 Aug 2021 Shuai Wang, Dachuan Li, Rui Wang, Qi Hao, Yik-Chung Wu, Derrick Wing Kwan Ng

Wireless federated learning (FL) is an emerging machine learning paradigm that trains a global parametric model from distributed datasets via wireless communications.

Federated Learning

Accelerating Federated Edge Learning via Optimized Probabilistic Device Scheduling

no code implementations24 Jul 2021 Maojun Zhang, Guangxu Zhu, Shuai Wang, Jiamo Jiang, Caijun Zhong, Shuguang Cui

Building on the analytical result, an optimized probabilistic scheduling policy is derived in closed-form by solving the approximate communication time minimization problem.

Autonomous Driving Learning Theory +1

Rethinking the Tradeoff in Integrated Sensing and Communication: Recognition Accuracy versus Communication Rate

no code implementations20 Jul 2021 Guoliang Li, Shuai Wang, Jie Li, Rui Wang, Fan Liu, Meihong Zhang, Xiaohui Peng, Tony Xiao Han

Integrated sensing and communication (ISAC) is a promising technology to improve the band-utilization efficiency via spectrum sharing or hardware sharing between radar and communication systems.

Accelerating Edge Intelligence via Integrated Sensing and Communication

no code implementations20 Jul 2021 Tong Zhang, Shuai Wang, Guoliang Li, Fan Liu, Guangxu Zhu, Rui Wang

Conventionally, the sensing and communication stages are executed sequentially, which results in excessive amount of dataset generation and uploading time.

Perception Matters: Detecting Perception Failures of VQA Models Using Metamorphic Testing

1 code implementation CVPR 2021 Yuanyuan Yuan, Shuai Wang, Mingyue Jiang, Tsong Yueh Chen

MetaVQA checks whether the answer to (i, q) satisfies metamorphic relationships (MRs), denoting perception consistency, with the composed answers of transformed questions and images.

DNN Testing Question Answering +1

Privileged Graph Distillation for Cold Start Recommendation

no code implementations31 May 2021 Shuai Wang, Kun Zhang, Le Wu, Haiping Ma, Richang Hong, Meng Wang

The teacher model is composed of a heterogeneous graph structure for warm users and items with privileged CF links.

Collaborative Filtering Recommendation Systems

AGMB-Transformer: Anatomy-Guided Multi-Branch Transformer Network for Automated Evaluation of Root Canal Therapy

1 code implementation2 May 2021 Yunxiang Li, Guodong Zeng, Yifan Zhang, Jun Wang, Qianni Zhang, Qun Jin, Lingling Sun, Qisi Lian, Neng Xia, Ruizi Peng, Kai Tang, Yaqi Wang, Shuai Wang

Accurate evaluation of the treatment result on X-ray images is a significant and challenging step in root canal therapy since the incorrect interpretation of the therapy results will hamper timely follow-up which is crucial to the patients' treatment outcome.

Anatomy General Classification

Wireless Sensing With Deep Spectrogram Network and Primitive Based Autoregressive Hybrid Channel Model

no code implementations21 Apr 2021 Guoliang Li, Shuai Wang, Jie Li, Rui Wang, Xiaohui Peng, Tony Xiao Han

Although wireless channel models can be adopted for dataset generation, current channel models are mostly designed for communication rather than sensing.

Scene Understanding

High-Resolution Segmentation of Tooth Root Fuzzy Edge Based on Polynomial Curve Fitting with Landmark Detection

no code implementations7 Mar 2021 Yunxiang Li, Yifan Zhang, Yaqi Wang, Shuai Wang, Ruizi Peng, Kai Tang, Qianni Zhang, Jun Wang, Qun Jin, Lingling Sun

As the most economical and routine auxiliary examination in the diagnosis of root canal treatment, oral X-ray has been widely used by stomatologists.

Semantic Segmentation

Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles

1 code implementation5 Mar 2021 Zijian Zhang, Shuai Wang, Yuncong Hong, Liangkai Zhou, Qi Hao

The technology of dynamic map fusion among networked vehicles has been developed to enlarge sensing ranges and improve sensing accuracies for individual vehicles.

Federated Learning Knowledge Distillation +2

Edge Federated Learning Via Unit-Modulus Over-The-Air Computation

no code implementations28 Jan 2021 Shuai Wang, Yuncong Hong, Rui Wang, Qi Hao, Yik-Chung Wu, Derrick Wing Kwan Ng

Simulation results show that the proposed UMAirComp framework with PAM algorithm achieves a smaller mean square error of model parameters' estimation, training loss, and test error compared with other benchmark schemes.

Autonomous Driving Federated Learning

On Secure Degrees of Freedom of the MIMO Interference Channel with Local Output Feedback

no code implementations3 Jan 2021 Tong Zhang, Yinfei Xu, Shuai Wang, Miaowen Wen, Rui Wang

This paper studies the problem of sum-secure degrees of freedom (SDoF) of the (M, M, N, N) multiple-input multiple-output (MIMO) interference channel with local output feedback, so as to build an information-theoretic foundation and provide practical transmission schemes for 6G-enabled vehicles-to-vehicles (V2V).

Information Theory Information Theory

F^2ed-Learning: Good Fences Make Good Neighbors

no code implementations1 Jan 2021 Lun Wang, Qi Pang, Shuai Wang, Dawn Song

In this paper, we present F^2ed-Learning, the first federated learning protocol simultaneously defending against both semi-honest server and Byzantine malicious clients.

Federated Learning

A General Recurrent Tracking Framework Without Real Data

no code implementations ICCV 2021 Shuai Wang, Hao Sheng, Yang Zhang, Yubin Wu, Zhang Xiong

Based on this framework, a Recurrent Tracking Unit (RTU) is designed to score potential tracks through long-term information.

Multi-Object Tracking

Private Image Reconstruction from System Side Channels Using Generative Models

2 code implementations ICLR 2021 Yuanyuan Yuan, Shuai Wang, Junping Zhang

Given the ever-growing adoption of machine learning as a service (MLaaS), image analysis software on cloud platforms has been exploited by reconstructing private user images from system side channels.

Image Reconstruction Side Channel Analysis

Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks

no code implementations25 Dec 2020 Shanfeng Huang, Shuai Wang, Rui Wang, Miaowen Wen, Kaibin Huang

The ever-growing popularity and rapid improving of artificial intelligence (AI) have raised rethinking on the evolution of wireless networks.

3D Object Detection Autonomous Driving +2

MT-Teql: Evaluating and Augmenting Consistency of Text-to-SQL Models with Metamorphic Testing

no code implementations21 Dec 2020 Pingchuan Ma, Shuai Wang

Envisioning the general difficulty for text-to-SQL models to preserve prediction consistency against linguistic and schema variations, we propose MT-Teql, a Metamorphic Testing-based framework for systematically evaluating and augmenting the consistency of TExt-to-SQL models.


Multi-Domain Multi-Task Rehearsal for Lifelong Learning

no code implementations14 Dec 2020 Fan Lyu, Shuai Wang, Wei Feng, Zihan Ye, Fuyuan Hu, Song Wang

Rehearsal, seeking to remind the model by storing old knowledge in lifelong learning, is one of the most effective ways to mitigate catastrophic forgetting, i. e., biased forgetting of previous knowledge when moving to new tasks.

Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-ray Images

1 code implementation11 Nov 2020 Jingxiong Li, Yaqi Wang, Shuai Wang, Jun Wang, Jun Liu, Qun Jin, Lingling Sun

Moreover, massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learning models.

COVID-19 Diagnosis General Classification

Learning Centric Wireless Resource Allocation for Edge Computing: Algorithm and Experiment

no code implementations29 Oct 2020 Liangkai Zhou, Yuncong Hong, Shuai Wang, Ruihua Han, Dachuan Li, Rui Wang, Qi Hao

Edge intelligence is an emerging network architecture that integrates sensing, communication, computing components, and supports various machine learning applications, where a fundamental communication question is: how to allocate the limited wireless resources (such as time, energy) to the simultaneous model training of heterogeneous learning tasks?


A Knowledge-Driven Approach to Classifying Object and Attribute Coreferences in Opinion Mining

no code implementations Findings of the Association for Computational Linguistics 2020 Jiahua Chen, Shuai Wang, Sahisnu Mazumder, Bing Liu

Classifying and resolving coreferences of objects (e. g., product names) and attributes (e. g., product aspects) in opinionated reviews is crucial for improving the opinion mining performance.

Opinion Mining

Automatic Label Correction for the Accurate Edge Detection of Overlapping Cervical Cells

2 code implementations5 Oct 2020 Jiawei Liu, Qiang Wang, Huijie Fan, Shuai Wang, Wentao Li, Yandong Tang, Danbo Wang, Mingyi Zhou, Li Chen

The experiments on the dataset for training show that our automatic label correction algorithm can improve the accuracy of manual labels and further improve the positioning accuracy of overlapping cells with deep learning models.

Cell Segmentation Edge Detection

Towards Bidirectional Protection in Federated Learning

no code implementations2 Oct 2020 Lun Wang, Qi Pang, Shuai Wang, Dawn Song

At one end of the spectrum, some work uses secure aggregation techniques to hide the individual client's updates and only reveal the aggregated global update to a malicious server that strives to infer the clients' privacy from their updates.

Federated Learning

Edge Learning with Unmanned Ground Vehicle: Joint Path, Energy and Sample Size Planning

no code implementations7 Sep 2020 Dan Liu, Shuai Wang, Zhigang Wen, Lei Cheng, Miaowen Wen, Yik-Chung Wu

However, different devices may transmit different data for different machine learning jobs and a fundamental question is how to jointly plan the UGV path, the devices' energy consumption, and the number of samples for different jobs?

Edge-computing Machine Learning

Learning Centric Power Allocation for Edge Intelligence

no code implementations21 Jul 2020 Shuai Wang, Rui Wang, Qi Hao, Yik-Chung Wu, H. Vincent Poor

While machine-type communication (MTC) devices generate massive data, they often cannot process this data due to limited energy and computation power.


Improving Positive Unlabeled Learning: Practical AUL Estimation and New Training Method for Extremely Imbalanced Data Sets

no code implementations21 Apr 2020 Liwei Jiang, Dan Li, Qisheng Wang, Shuai Wang, Songtao Wang

Secondly, we propose ProbTagging, a new training method for extremely imbalanced data sets, where the number of unlabeled samples is hundreds or thousands of times that of positive samples.

MUTATT: Visual-Textual Mutual Guidance for Referring Expression Comprehension

no code implementations18 Mar 2020 Shuai Wang, Fan Lyu, Wei Feng, Song Wang

In this paper, we argue that for REC the referring expression and the target region are semantically correlated and subject, location and relationship consistency exist between vision and language. On top of this, we propose a novel approach called MutAtt to construct mutual guidance between vision and language, which treat vision and language equally thus yield compact information matching.

Referring Expression Referring Expression Comprehension

Intelligent Home 3D: Automatic 3D-House Design from Linguistic Descriptions Only

1 code implementation CVPR 2020 Qi Chen, Qi Wu, Rui Tang, Yu-Han Wang, Shuai Wang, Mingkui Tan

To this end, we propose a House Plan Generative Model (HPGM) that first translates the language input to a structural graph representation and then predicts the layout of rooms with a Graph Conditioned Layout Prediction Network (GC LPN) and generates the interior texture with a Language Conditioned Texture GAN (LCT-GAN).

Federated Matrix Factorization: Algorithm Design and Application to Data Clustering

no code implementations12 Feb 2020 Shuai Wang, Tsung-Hui Chang

Recent demands on data privacy have called for federated learning (FL) as a new distributed learning paradigm in massive and heterogeneous networks.

Federated Learning

Metamorphic Testing for Object Detection Systems

no code implementations19 Dec 2019 Shuai Wang, Zhendong Su

To fill this critical gap, we introduce the design and realization of MetaOD, the first metamorphic testing system for object detectors to effectively reveal erroneous detection results by commercial object detectors.

Autonomous Driving object-detection +1

Machine Intelligence at the Edge with Learning Centric Power Allocation

no code implementations12 Nov 2019 Shuai Wang, Yik-Chung Wu, Minghua Xia, Rui Wang, H. Vincent Poor

However, power allocation in this paradigm requires maximizing the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient.

Fairness Learning Theory

Building an Application Independent Natural Language Interface

no code implementations30 Oct 2019 Sahisnu Mazumder, Bing Liu, Shuai Wang, Sepideh Esmaeilpour

Traditional approaches to building natural language (NL) interfaces typically use a semantic parser to parse the user command and convert it to a logical form, which is then translated to an executable action in an application.

Lifelong and Interactive Learning of Factual Knowledge in Dialogues

no code implementations WS 2019 Sahisnu Mazumder, Bing Liu, Shuai Wang, Nianzu Ma

Dialogue systems are increasingly using knowledge bases (KBs) storing real-world facts to help generate quality responses.

Margin Matters: Towards More Discriminative Deep Neural Network Embeddings for Speaker Recognition

no code implementations18 Jun 2019 Xu Xiang, Shuai Wang, Houjun Huang, Yanmin Qian, Kai Yu

The proposed approach can achieve the state-of-the-art performance, with 25% ~ 30% equal error rate (EER) reduction on both tasks when compared to strong baselines using cross entropy loss with softmax, obtaining 2. 238% EER on VoxCeleb1 test set and 2. 761% EER on SITW core-core test set, respectively.

Speaker Recognition

Forward and Backward Knowledge Transfer for Sentiment Classification

no code implementations8 Jun 2019 Hao Wang, Bing Liu, Shuai Wang, Nianzu Ma, Yan Yang

That is, it is possible to improve the NB classifier for a task by improving its model parameters directly by using the retained knowledge from other tasks.

Classification General Classification +2

Clustering by Orthogonal NMF Model and Non-Convex Penalty Optimization

1 code implementation3 Jun 2019 Shuai Wang, Tsung-Hui Chang, Ying Cui, Jong-Shi Pang

We then apply a non-convex penalty (NCP) approach to add them to the objective as penalty terms, leading to a problem that is efficiently solvable.

BML: A High-performance, Low-cost Gradient Synchronization Algorithm for DML Training

no code implementations NeurIPS 2018 Songtao Wang, Dan Li, Yang Cheng, Jinkun Geng, Yanshu Wang, Shuai Wang, Shu-Tao Xia, Jianping Wu

In distributed machine learning (DML), the network performance between machines significantly impacts the speed of iterative training.

Guided Exploration in Deep Reinforcement Learning

no code implementations27 Sep 2018 Sahisnu Mazumder, Bing Liu, Shuai Wang, Yingxuan Zhu, Xiaotian Yin, Lifeng Liu, Jian Li, Yongbing Huang

This paper proposes a new method to drastically speed up deep reinforcement learning (deep RL) training for problems that have the property of \textit{state-action permissibility} (SAP).


Deep Learning for Sentiment Analysis : A Survey

1 code implementation24 Jan 2018 Lei Zhang, Shuai Wang, Bing Liu

Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results.

Machine Learning Sentiment Analysis

Contextual and Position-Aware Factorization Machines for Sentiment Classification

no code implementations18 Jan 2018 Shuai Wang, Mianwei Zhou, Geli Fei, Yi Chang, Bing Liu

While existing machine learning models have achieved great success for sentiment classification, they typically do not explicitly capture sentiment-oriented word interaction, which can lead to poor results for fine-grained analysis at the snippet level (a phrase or sentence).

Classification General Classification +3

Translingual Obfuscation

no code implementations5 Jan 2016 Pei Wang, Shuai Wang, Jiang Ming, Yufei Jiang, Dinghao Wu

We introduce translingual obfuscation, a new software obfuscation scheme which makes programs obscure by "misusing" the unique features of certain programming languages.

Cryptography and Security Software Engineering

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