Search Results for author: Sheng Li

Found 64 papers, 6 papers with code

CAN-GRU: a Hierarchical Model for Emotion Recognition in Dialogue

no code implementations CCL 2020 Ting Jiang, Bing Xu, Tiejun Zhao, Sheng Li

In the first layer, in order to extract textual features of utterances, we propose a convolutional self-attention network(CAN).

Emotion Recognition Opinion Mining

Exploiting Auxiliary Data for Offensive Language Detection with Bidirectional Transformers

no code implementations ACL (WOAH) 2021 Sumer Singh, Sheng Li

Our approach introduces domain adaptation (DA) training procedures to ALBERT, such that it can effectively exploit auxiliary data from source domains to improve the OLD performance in a target domain.

Domain Adaptation

Fusion of Self-supervised Learned Models for MOS Prediction

no code implementations11 Apr 2022 Zhengdong Yang, Wangjin Zhou, Chenhui Chu, Sheng Li, Raj Dabre, Raphael Rubino, Yi Zhao

This challenge aims to predict MOS scores of synthetic speech on two tracks, the main track and a more challenging sub-track: out-of-domain (OOD).

Multi-Task Adversarial Learning for Treatment Effect Estimation in Basket Trials

no code implementations10 Mar 2022 Zhixuan Chu, Stephen L. Rathbun, Sheng Li

In our paper, the basket trial is employed as an intuitive example to present this new causal inference setting.

Causal Inference Representation Learning +1

Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data

no code implementations22 Feb 2022 Zhixuan Chu, Stephen Rathbun, Sheng Li

In this paper, we reveal the weaknesses of these strategies, i. e., they lead to the loss of predictive information when enforcing the domain invariance; and the treatment effect estimation performance is unstable, which heavily relies on the characteristics of the domain distributions and the choice of domain divergence metrics.

Causal Inference Representation Learning +1

Invertible Image Dataset Protection

no code implementations29 Dec 2021 Kejiang Chen, Xianhan Zeng, Qichao Ying, Sheng Li, Zhenxing Qian, Xinpeng Zhang

We develop a reversible adversarial example generator (RAEG) that introduces slight changes to the images to fool traditional classification models.

Adversarial Defense

GenReg: Deep Generative Method for Fast Point Cloud Registration

no code implementations23 Nov 2021 Xiaoshui Huang, Zongyi Xu, Guofeng Mei, Sheng Li, Jian Zhang, Yifan Zuo, Yucheng Wang

To solve this challenge, we propose a new data-driven registration algorithm by investigating deep generative neural networks to point cloud registration.

Point Cloud Registration

On Generating Identifiable Virtual Faces

no code implementations15 Oct 2021 Zhuowen Yuan, Sheng Li, Xinpeng Zhang, Zhenxin Qian, Alex Kot

Our virtual face images are visually different from the original ones for privacy protection.

Face Anonymization Face Recognition +1

CrossMatch: Cross-Classifier Consistency Regularization for Open-Set Single Domain Generalization

no code implementations ICLR 2022 Ronghang Zhu, Sheng Li

In this paper, we propose a challenging and untouched problem: \textit{Open-Set Single Domain Generalization} (OS-SDG), where target domains include unseen categories out of source label space.

Data Augmentation Domain Generalization

Pairwise Adversarial Training for Unsupervised Class-imbalanced Domain Adaptation

no code implementations29 Sep 2021 Weili Shi, Ronghang Zhu, Sheng Li

In this paper, we propose a pairwise adversarial training approach to augment training data for unsupervised class-imbalanced domain adaptation.

Transfer Learning Unsupervised Domain Adaptation

Automated Graph Learning via Population Based Self-Tuning GCN

no code implementations9 Jul 2021 Ronghang Zhu, Zhiqiang Tao, Yaliang Li, Sheng Li

Owing to the remarkable capability of extracting effective graph embeddings, graph convolutional network (GCN) and its variants have been successfully applied to a broad range of tasks, such as node classification, link prediction, and graph classification.

Graph Classification Graph Learning +3

Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search

no code implementations8 Jun 2021 Ziyu Guan, Hongchang Wu, Qingyu Cao, Hao liu, Wei Zhao, Sheng Li, Cai Xu, Guang Qiu, Jian Xu, Bo Zheng

Although a few studies use multi-agent reinforcement learning to set up a cooperative game, they still suffer the following drawbacks: (1) They fail to avoid collusion solutions where all the advertisers involved in an auction collude to bid an extremely low price on purpose.

Multi-agent Reinforcement Learning

Safe reopening of university campuses is possible with COVID-19 vaccination

no code implementations13 May 2021 Matthew Junge, Sheng Li, Samitha Samaranayake, Matthew Zalesak

We construct an agent-based SEIR model to simulate COVID-19 spread at a 16000-student mostly non-residential urban university during the Fall 2021 Semester.

Searching for Fast Model Families on Datacenter Accelerators

no code implementations CVPR 2021 Sheng Li, Mingxing Tan, Ruoming Pang, Andrew Li, Liqun Cheng, Quoc Le, Norman P. Jouppi

On top of our DC accelerator optimized neural architecture search space, we further propose a latency-aware compound scaling (LACS), the first multi-objective compound scaling method optimizing both accuracy and latency.

Neural Architecture Search

Continual Lifelong Causal Effect Inference with Real World Evidence

no code implementations1 Jan 2021 Zhixuan Chu, Stephen Rathbun, Sheng Li

We propose a Continual Causal Effect Representation Learning method for estimating causal effect with observational data, which are incrementally available from non-stationary data distributions.

Representation Learning Selection bias

Transferable Feature Learning on Graphs Across Visual Domains

no code implementations1 Jan 2021 Ronghang Zhu, Xiaodong Jiang, Jiasen Lu, Sheng Li

In this paper, we propose a novel Transferable Feature Learning approach on Graphs (TFLG) for unsupervised adversarial domain adaptation, which jointly incorporates sample-level and class-level structure information across two domains.

Unsupervised Domain Adaptation

Co-embedding of Nodes and Edges with Graph Neural Networks

no code implementations25 Oct 2020 Xiaodong Jiang, Ronghang Zhu, Pengsheng Ji, Sheng Li

CensNet is a general graph embedding framework, which embeds both nodes and edges to a latent feature space.

Graph Classification Graph Embedding +5

Matching in Selective and Balanced Representation Space for Treatment Effects Estimation

no code implementations15 Sep 2020 Zhixuan Chu, Stephen L. Rathbun, Sheng Li

The dramatically growing availability of observational data is being witnessed in various domains of science and technology, which facilitates the study of causal inference.

Causal Inference Representation Learning +1

Collaborative Attention Mechanism for Multi-View Action Recognition

no code implementations14 Sep 2020 Yue Bai, Zhiqiang Tao, Lichen Wang, Sheng Li, Yu Yin, Yun Fu

Extensive experiments on four action datasets illustrate the proposed CAM achieves better results for each view and also boosts multi-view performance.

Action Recognition Frame +1

Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning

1 code implementation19 Jun 2020 Sheng Li, Jayesh K. Gupta, Peter Morales, Ross Allen, Mykel J. Kochenderfer

Coordination graph based formalization allows reasoning about the joint action based on the structure of interactions.

reinforcement-learning SMAC +2

Stereotype-Free Classification of Fictitious Faces

no code implementations29 Apr 2020 Mohammadhossein Toutiaee, Soheyla Amirian, John A. Miller, Sheng Li

The proposed approach aids labeling new data (fictitious output images) by minimizing a penalized version of the least squares cost function between realistic pictures and target pictures.

Classification Fairness +1

A Survey on Causal Inference

1 code implementation5 Feb 2020 Liuyi Yao, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, Aidong Zhang

Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up.

Causal Inference

Cross-scale Attention Model for Acoustic Event Classification

no code implementations27 Dec 2019 Xugang Lu, Peng Shen, Sheng Li, Yu Tsao, Hisashi Kawai

However, a potential limitation of the network is that the discriminative features from the bottom layers (which can model the short-range dependency) are smoothed out in the final representation.

Classification General Classification

Learning Hybrid Representation by Robust Dictionary Learning in Factorized Compressed Space

no code implementations26 Dec 2019 Jiahuan Ren, Zhao Zhang, Sheng Li, Yang Wang, Guangcan Liu, Shuicheng Yan, Meng Wang

Specifically, J-RFDL performs the robust representation by DL in a factorized compressed space to eliminate the negative effects of noise and outliers on the results, which can also make the DL process efficient.

Dictionary Learning

Optimizing Collision Avoidance in Dense Airspace using Deep Reinforcement Learning

no code implementations20 Dec 2019 Sheng Li, Maxim Egorov, Mykel Kochenderfer

New methodologies will be needed to ensure the airspace remains safe and efficient as traffic densities rise to accommodate new unmanned operations.

reinforcement-learning

Correlative Channel-Aware Fusion for Multi-View Time Series Classification

no code implementations24 Nov 2019 Yue Bai, Lichen Wang, Zhiqiang Tao, Sheng Li, Yun Fu

Multi-view time series classification (MVTSC) aims to improve the performance by fusing the distinctive temporal information from multiple views.

Classification General Classification +2

Learning Robust Data Representation: A Knowledge Flow Perspective

no code implementations28 Sep 2019 Zhengming Ding, Ming Shao, Handong Zhao, Sheng Li

It is always demanding to learn robust visual representation for various learning problems; however, this learning and maintenance process usually suffers from noise, incompleteness or knowledge domain mismatch.

Representation Learning Transfer Learning

Flexible Auto-weighted Local-coordinate Concept Factorization: A Robust Framework for Unsupervised Clustering

no code implementations2 Sep 2019 Zhao Zhang, Yan Zhang, Sheng Li, Guangcan Liu, Dan Zeng, Shuicheng Yan, Meng Wang

For auto-weighting, RFA-LCF jointly preserves the manifold structures in the basis concept space and new coordinate space in an adaptive manner by minimizing the reconstruction errors on clean data, anchor points and coordinates.

Adaptive Structure-constrained Robust Latent Low-Rank Coding for Image Recovery

no code implementations21 Aug 2019 Zhao Zhang, Lei Wang, Sheng Li, Yang Wang, Zheng Zhang, Zheng-Jun Zha, Meng Wang

Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse error part.

Representation Learning

CensNet: Convolution with Edge-Node Switching in Graph Neural Networks

no code implementations Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19) 2019 Xiaodong Jiang, Pengsheng Ji, Sheng Li

In this paper, we present CensNet, Convolution with Edge-Node Switching graph neural network, for semi-supervised classification and regression in graph-structured data with both node and edge features.

 Ranked #1 on Graph Regression on Lipophilicity (RMSE@80%Train metric)

Graph Classification Graph Embedding +2

Robust Subspace Discovery by Block-diagonal Adaptive Locality-constrained Representation

no code implementations4 Aug 2019 Zhao Zhang, Jiahuan Ren, Sheng Li, Richang Hong, Zheng-Jun Zha, Meng Wang

Leveraging on the Frobenius-norm based latent low-rank representation model, rBDLR jointly learns the coding coefficients and salient features, and improves the results by enhancing the robustness to outliers and errors in given data, preserving local information of salient features adaptively and ensuring the block-diagonal structures of the coefficients.

Representation Learning

Scalable Block-Diagonal Locality-Constrained Projective Dictionary Learning

no code implementations25 May 2019 Zhao Zhang, Weiming Jiang, Zheng Zhang, Sheng Li, Guangcan Liu, Jie Qin

More importantly, LC-PDL avoids using the complementary data matrix to learn the sub-dictionary over each class.

Dictionary Learning

Robust Unsupervised Flexible Auto-weighted Local-Coordinate Concept Factorization for Image Clustering

no code implementations25 May 2019 Zhao Zhang, Yan Zhang, Sheng Li, Guangcan Liu, Meng Wang, Shuicheng Yan

RFA-LCF integrates the robust flexible CF, robust sparse local-coordinate coding and the adaptive reconstruction weighting learning into a unified model.

Image Clustering Representation Learning

SADIH: Semantic-Aware DIscrete Hashing

no code implementations3 Apr 2019 Zheng Zhang, Guo-Sen Xie, Yang Li, Sheng Li, Zi Huang

Due to its low storage cost and fast query speed, hashing has been recognized to accomplish similarity search in large-scale multimedia retrieval applications.

Scene Graph Generation with External Knowledge and Image Reconstruction

no code implementations CVPR 2019 Jiuxiang Gu, Handong Zhao, Zhe Lin, Sheng Li, Jianfei Cai, Mingyang Ling

Scene graph generation has received growing attention with the advancements in image understanding tasks such as object detection, attributes and relationship prediction,~\etc.

Graph Generation Image Reconstruction +3

Supervised Transfer Learning for Product Information Question Answering

no code implementations8 Jan 2019 Tuan Manh Lai, Trung Bui, Nedim Lipka, Sheng Li

Popular e-commerce websites such as Amazon offer community question answering systems for users to pose product related questions and experienced customers may provide answers voluntarily.

Community Question Answering Transfer Learning

Representation Learning for Treatment Effect Estimation from Observational Data

1 code implementation NeurIPS 2018 Liuyi Yao, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, Aidong Zhang

Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due to the missing counterfactuals and the selection bias.

Causal Inference Representation Learning +1

X-GANs: Image Reconstruction Made Easy for Extreme Cases

no code implementations6 Aug 2018 Longfei Liu, Sheng Li, Yisong Chen, Guoping Wang

Image reconstruction including image restoration and denoising is a challenging problem in the field of image computing.

Image Compression Image Denoising +3

A Review on Deep Learning Techniques Applied to Answer Selection

no code implementations COLING 2018 Tuan Manh Lai, Trung Bui, Sheng Li

Given a question and a set of candidate answers, answer selection is the task of identifying which of the candidates answers the question correctly.

Answer Selection Community Question Answering +3

Supervised Treebank Conversion: Data and Approaches

no code implementations ACL 2018 Xinzhou Jiang, Zhenghua Li, Bo Zhang, Min Zhang, Sheng Li, Luo Si

Treebank conversion is a straightforward and effective way to exploit various heterogeneous treebanks for boosting parsing performance.

Dependency Parsing Multi-Task Learning

Matching on Balanced Nonlinear Representations for Treatment Effects Estimation

no code implementations NeurIPS 2017 Sheng Li, Yun Fu

Estimating treatment effects from observational data is challenging due to the missing counterfactuals.

Domain Adaptation

Enabling Sparse Winograd Convolution by Native Pruning

1 code implementation28 Feb 2017 Sheng Li, Jongsoo Park, Ping Tak Peter Tang

Sparse methods and the use of Winograd convolutions are two orthogonal approaches, each of which significantly accelerates convolution computations in modern CNNs.

Parallelizing Word2Vec in Multi-Core and Many-Core Architectures

1 code implementation18 Nov 2016 Shihao Ji, Nadathur Satish, Sheng Li, Pradeep Dubey

Word2vec is a widely used algorithm for extracting low-dimensional vector representations of words.

Faster CNNs with Direct Sparse Convolutions and Guided Pruning

1 code implementation4 Aug 2016 Jongsoo Park, Sheng Li, Wei Wen, Ping Tak Peter Tang, Hai Li, Yiran Chen, Pradeep Dubey

Pruning CNNs in a way that increase inference speed often imposes specific sparsity structures, thus limiting the achievable sparsity levels.

Parallelizing Word2Vec in Shared and Distributed Memory

no code implementations15 Apr 2016 Shihao Ji, Nadathur Satish, Sheng Li, Pradeep Dubey

In combination, these techniques allow us to scale up the computation near linearly across cores and nodes, and process hundreds of millions of words per second, which is the fastest word2vec implementation to the best of our knowledge.

Machine Translation Named Entity Recognition +3

Temporal Subspace Clustering for Human Motion Segmentation

no code implementations ICCV 2015 Sheng Li, Kang Li, Yun Fu

Subspace clustering is an effective technique for segmenting data drawn from multiple subspaces.

Motion Segmentation Time Series

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