Search Results for author: Rui Song

Found 90 papers, 37 papers with code

单项形容词定语分布考察及“的”字隐现研究(Study on Distribution of Single Item Adjective Attributives and Appearance and Disappearance of “de”)

no code implementations CCL 2022 Rui Song, Zhimin Wang

“本文以2019-2021年《人民日报》文章中单项形容词定语77845个词例为研究对象, 从实用性的角度考察了粘合式与组合式定语词例的分布特征、音节组配模式及“的”字的隐现倾向性。通过研究我们发现, 粘合式定语的词例数量明显少于组合式定语词例数量, 但使用频数却高出组合式定语的4-5倍。两种定语结构中, 形容词和名词重复使用的比例很高, 但其共现组合的比例偏少, 同时, 真实文本中“的”字的隐现具有“两极分化”的特征, 绝大部分词例在使用过程中带“的”或不带“的”都具有很强的倾向性,“的”字出现具有区分词义和突显信息的作用,“的”字隐藏能促使语义更加凝练, 进一步固化句式结构, 使得某些句式形成了特指或隐喻的表达方式。本文为形容词定语结构的词汇语义研究提供依据和参考。”

On Validation and Planning of An Optimal Decision Rule with Application in Healthcare Studies

no code implementations ICML 2020 Hengrui Cai, Wenbin Lu, Rui Song

Estimation of optimal decision rules (ODR) has been extensively investigated recently, however, at present, no testing procedure is proposed to verify whether these ODRs are significantly better than the naive decision rule that always assigning individuals to a fixed treatment option.

TUMTraf V2X Cooperative Perception Dataset

3 code implementations2 Mar 2024 Walter Zimmer, Gerhard Arya Wardana, Suren Sritharan, Xingcheng Zhou, Rui Song, Alois C. Knoll

We propose CoopDet3D, a cooperative multi-modal fusion model, and TUMTraf-V2X, a perception dataset, for the cooperative 3D object detection and tracking task.

3D Object Detection Autonomous Vehicles +1

Collaborative Semantic Occupancy Prediction with Hybrid Feature Fusion in Connected Automated Vehicles

no code implementations12 Feb 2024 Rui Song, Chenwei Liang, Hu Cao, Zhiran Yan, Walter Zimmer, Markus Gross, Andreas Festag, Alois Knoll

Additionally, due to the lack of a collaborative perception dataset designed for semantic occupancy prediction, we augment a current collaborative perception dataset to include 3D collaborative semantic occupancy labels for a more robust evaluation.

3D Semantic Occupancy Prediction

Is Knowledge All Large Language Models Needed for Causal Reasoning?

1 code implementation30 Dec 2023 Hengrui Cai, ShengJie Liu, Rui Song

This paper explores the causal reasoning of large language models (LLMs) to enhance their interpretability and reliability in advancing artificial intelligence.

counterfactual

Large Language Model for Causal Decision Making

no code implementations28 Dec 2023 Haitao Jiang, Lin Ge, Yuhe Gao, Jianian Wang, Rui Song

Large Language Models (LLMs) have shown their success in language understanding and reasoning on general topics.

Decision Making Language Modelling +2

TACIT: A Target-Agnostic Feature Disentanglement Framework for Cross-Domain Text Classification

1 code implementation25 Dec 2023 Rui Song, Fausto Giunchiglia, Yingji Li, Mingjie Tian, Hao Xu

However, these methods rely on unlabeled samples provided by the target domains, which renders the model ineffective when the target domain is agnostic.

Cross-Domain Text Classification Disentanglement +2

Zero-Inflated Bandits

no code implementations25 Dec 2023 Haoyu Wei, Runzhe Wan, Lei Shi, Rui Song

Many real applications of bandits have sparse non-zero rewards, leading to slow learning rates.

Thompson Sampling

Effect Size Estimation for Duration Recommendation in Online Experiments: Leveraging Hierarchical Models and Objective Utility Approaches

no code implementations20 Dec 2023 Yu Liu, Runzhe Wan, James McQueen, Doug Hains, Jinxiang Gu, Rui Song

The selection of the assumed effect size (AES) critically determines the duration of an experiment, and hence its accuracy and efficiency.

Decision Making

A Survey on Fairness in Large Language Models

no code implementations20 Aug 2023 Yingji Li, Mengnan Du, Rui Song, Xin Wang, Ying Wang

Large Language Models (LLMs) have shown powerful performance and development prospects and are widely deployed in the real world.

Fairness

Pseudo Flow Consistency for Self-Supervised 6D Object Pose Estimation

1 code implementation ICCV 2023 Yang Hai, Rui Song, Jiaojiao Li, David Ferstl, Yinlin Hu

Most self-supervised 6D object pose estimation methods can only work with additional depth information or rely on the accurate annotation of 2D segmentation masks, limiting their application range.

6D Pose Estimation using RGB

Automatic Counterfactual Augmentation for Robust Text Classification Based on Word-Group Search

no code implementations1 Jul 2023 Rui Song, Fausto Giunchiglia, Yingji Li, Hao Xu

Despite large-scale pre-trained language models have achieved striking results for text classificaion, recent work has raised concerns about the challenge of shortcut learning.

counterfactual Fairness +3

FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems

1 code implementation4 Apr 2023 Rui Song, Runsheng Xu, Andreas Festag, Jiaqi Ma, Alois Knoll

Our findings suggest that FedBEVT outperforms the baseline approaches in all four use cases, demonstrating the potential of our approach for improving BEV perception in autonomous driving.

Autonomous Driving Federated Learning

Experimentation Platforms Meet Reinforcement Learning: Bayesian Sequential Decision-Making for Continuous Monitoring

no code implementations2 Apr 2023 Runzhe Wan, Yu Liu, James McQueen, Doug Hains, Rui Song

With the growing needs of online A/B testing to support the innovation in industry, the opportunity cost of running an experiment becomes non-negligible.

Decision Making reinforcement-learning

Rigidity-Aware Detection for 6D Object Pose Estimation

2 code implementations CVPR 2023 Yang Hai, Rui Song, Jiaojiao Li, Mathieu Salzmann, Yinlin Hu

To address this, we propose a rigidity-aware detection method exploiting the fact that, in 6D pose estimation, the target objects are rigid.

6D Pose Estimation 6D Pose Estimation using RGB +3

Multiplier Bootstrap-based Exploration

no code implementations3 Feb 2023 Runzhe Wan, Haoyu Wei, Branislav Kveton, Rui Song

Despite the great interest in the bandit problem, designing efficient algorithms for complex models remains challenging, as there is typically no analytical way to quantify uncertainty.

Multi-Armed Bandits

A Reinforcement Learning Framework for Dynamic Mediation Analysis

1 code implementation31 Jan 2023 Lin Ge, Jitao Wang, Chengchun Shi, Zhenke Wu, Rui Song

However, there are a number of applications (e. g., mobile health) where the treatments are sequentially assigned over time and the dynamic mediation effects are of primary interest.

reinforcement-learning Reinforcement Learning (RL)

On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs

1 code implementation29 Jan 2023 Richard A Watson, Hengrui Cai, Xinming An, Samuel McLean, Rui Song

To characterize this heterogeneity, we first conceptualize heterogeneous causal graphs (HCGs) by generalizing the causal graphical model with confounder-based interactions and multiple mediators.

Deep Spectral Q-learning with Application to Mobile Health

no code implementations3 Jan 2023 Yuhe Gao, Chengchun Shi, Rui Song

Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates.

Q-Learning

Efficient Hierarchical Entropy Model for Learned Point Cloud Compression

no code implementations CVPR 2023 Rui Song, Chunyang Fu, Shan Liu, Ge Li

Learning an accurate entropy model is a fundamental way to remove the redundancy in point cloud compression.

FCC: Feature Clusters Compression for Long-Tailed Visual Recognition

1 code implementation CVPR 2023 Jian Li, Ziyao Meng, Daqian Shi, Rui Song, Xiaolei Diao, Jingwen Wang, Hao Xu

Through representation learning, DNNs can map BFs into dense clusters in feature space, while the features of minority classes often show sparse clusters.

Representation Learning

Heterogeneous Synthetic Learner for Panel Data

no code implementations30 Dec 2022 Ye Shen, Runzhe Wan, Hengrui Cai, Rui Song

In the new era of personalization, learning the heterogeneous treatment effect (HTE) becomes an inevitable trend with numerous applications.

Quantile Off-Policy Evaluation via Deep Conditional Generative Learning

no code implementations29 Dec 2022 Yang Xu, Chengchun Shi, Shikai Luo, Lan Wang, Rui Song

Off-Policy evaluation (OPE) is concerned with evaluating a new target policy using offline data generated by a potentially different behavior policy.

Decision Making Off-policy evaluation

An Instrumental Variable Approach to Confounded Off-Policy Evaluation

no code implementations29 Dec 2022 Yang Xu, Jin Zhu, Chengchun Shi, Shikai Luo, Rui Song

Off-policy evaluation (OPE) is a method for estimating the return of a target policy using some pre-collected observational data generated by a potentially different behavior policy.

Decision Making Off-policy evaluation

Mining the Factor Zoo: Estimation of Latent Factor Models with Sufficient Proxies

no code implementations25 Dec 2022 Runzhe Wan, YingYing Li, Wenbin Lu, Rui Song

Latent factor model estimation typically relies on either using domain knowledge to manually pick several observed covariates as factor proxies, or purely conducting multivariate analysis such as principal component analysis.

regression

ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals

no code implementations11 Dec 2022 Rui Song, Liguo Zhou, Lingjuan Lyu, Andreas Festag, Alois Knoll

To address this bottleneck, we introduce a residual-based federated learning framework (ResFed), where residuals rather than model parameters are transmitted in communication networks for training.

Federated Learning Quantization

Few-shot Learning with Class-Covariance Metric for Hyperspectral Image Classification

1 code implementation journal 2022 Bobo Xi, Jiaojiao Li, Yunsong Li, Rui Song, Danfeng Hong, Jocelyn Chanussot.

Recently, embedding and metric-based few-shot learning (FSL) has been introduced into hyperspectral image classification (HSIC) and achieved impressive progress.

Few-Shot Learning Hyperspectral Image Classification

Edge-Aided Sensor Data Sharing in Vehicular Communication Networks

no code implementations17 Jun 2022 Rui Song, Anupama Hegde, Numan Senel, Alois Knoll, Andreas Festag

Specifically, when the measurement error from the sensors (also referred as measurement noise) is unknown and time varying, the performance of the data fusion process is restricted, which represents a major challenge in the calibration of sensors.

Noise Estimation

Locally Aggregated Feature Attribution on Natural Language Model Understanding

no code implementations NAACL 2022 Sheng Zhang, Jin Wang, Haitao Jiang, Rui Song

Some feature attribution methods have shown promising results in computer vision, especially the gradient-based methods where effectively smoothing the gradients with reference data is key to a robust and faithful result.

Language Modelling Sentiment Analysis

Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS

1 code implementation1 Apr 2022 Rui Song, Liguo Zhou, Venkatnarayanan Lakshminarasimhan, Andreas Festag, Alois Knoll

Considering the individual heterogeneity of data distribution, computational and communication capabilities across traffic agents and roadside units, we employ a novel method that addresses the heterogeneity of different aggregation layers of the framework architecture, i. e., aggregation in layers of roadside units and cloud.

Autonomous Vehicles Federated Learning

Adaptive Semi-Supervised Inference for Optimal Treatment Decisions with Electronic Medical Record Data

no code implementations4 Mar 2022 Kevin Gunn, Wenbin Lu, Rui Song

Simulation studies are conducted to assess the empirical performance of the proposed method and to compare with a fully supervised method using only the labeled data.

Imputation

Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework

no code implementations26 Feb 2022 Runzhe Wan, Lin Ge, Rui Song

In this paper, we propose a unified meta-learning framework for a general class of structured bandit problems where the parameter space can be factorized to item-level.

Meta-Learning Thompson Sampling

Safe Exploration for Efficient Policy Evaluation and Comparison

no code implementations26 Feb 2022 Runzhe Wan, Branislav Kveton, Rui Song

High-quality data plays a central role in ensuring the accuracy of policy evaluation.

Safe Exploration

Statistically Efficient Advantage Learning for Offline Reinforcement Learning in Infinite Horizons

1 code implementation26 Feb 2022 Chengchun Shi, Shikai Luo, Yuan Le, Hongtu Zhu, Rui Song

We consider reinforcement learning (RL) methods in offline domains without additional online data collection, such as mobile health applications.

reinforcement-learning Reinforcement Learning (RL)

On Learning and Testing of Counterfactual Fairness through Data Preprocessing

no code implementations25 Feb 2022 Haoyu Chen, Wenbin Lu, Rui Song, Pulak Ghosh

Machine learning has become more important in real-life decision-making but people are concerned about the ethical problems it may bring when used improperly.

BIG-bench Machine Learning counterfactual +2

Exploratory Hidden Markov Factor Models for Longitudinal Mobile Health Data: Application to Adverse Posttraumatic Neuropsychiatric Sequelae

no code implementations25 Feb 2022 Lin Ge, Xinming An, Donglin Zeng, Samuel McLean, Ronald Kessler, Rui Song

Adverse posttraumatic neuropsychiatric sequelae (APNS) are common among veterans and millions of Americans after traumatic exposures, resulting in substantial burdens for trauma survivors and society.

Heart Rate Variability Model Selection

Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process

1 code implementation22 Feb 2022 Chengchun Shi, Jin Zhu, Ye Shen, Shikai Luo, Hongtu Zhu, Rui Song

In this paper, we show that with some auxiliary variables that mediate the effect of actions on the system dynamics, the target policy's value is identifiable in a confounded Markov decision process.

Uncertainty Quantification

A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets

1 code implementation21 Feb 2022 Chengchun Shi, Runzhe Wan, Ge Song, Shikai Luo, Rui Song, Hongtu Zhu

In this paper we consider large-scale fleet management in ride-sharing companies that involve multiple units in different areas receiving sequences of products (or treatments) over time.

Management Multi-agent Reinforcement Learning +1

Rule Mining over Knowledge Graphs via Reinforcement Learning

no code implementations21 Feb 2022 Lihan Chen, Sihang Jiang, Jingping Liu, Chao Wang, Sheng Zhang, Chenhao Xie, Jiaqing Liang, Yanghua Xiao, Rui Song

Knowledge graphs (KGs) are an important source repository for a wide range of applications and rule mining from KGs recently attracts wide research interest in the KG-related research community.

Knowledge Graphs reinforcement-learning +1

OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression

1 code implementation12 Feb 2022 Chunyang Fu, Ge Li, Rui Song, Wei Gao, Shan Liu

In point cloud compression, sufficient contexts are significant for modeling the point cloud distribution.

Reinforcement Learning with Heterogeneous Data: Estimation and Inference

no code implementations31 Jan 2022 Elynn Y. Chen, Rui Song, Michael I. Jordan

Reinforcement Learning (RL) has the promise of providing data-driven support for decision-making in a wide range of problems in healthcare, education, business, and other domains.

Decision Making reinforcement-learning +1

Statistical Learning for Individualized Asset Allocation

no code implementations20 Jan 2022 Yi Ding, YingYing Li, Rui Song

We show that our proposed Discretization and Regression with generalized fOlded concaVe penalty on Effect discontinuity (DROVE) approach enjoys desirable theoretical properties and allows for statistical inference of the optimal value associated with optimal decision-making.

Decision Making regression

HPRN: Holistic Prior-embedded Relation Network for Spectral Super-Resolution

1 code implementation29 Dec 2021 Chaoxiong Wu, Jiaojiao Li, Rui Song, Yunsong Li, Qian Du

Spectral super-resolution (SSR) refers to the hyperspectral image (HSI) recovery from an RGB counterpart.

Relation Relation Network +2

A Review on Graph Neural Network Methods in Financial Applications

no code implementations27 Nov 2021 Jianian Wang, Sheng Zhang, Yanghua Xiao, Rui Song

With multiple components and relations, financial data are often presented as graph data, since it could represent both the individual features and the complicated relations.

Jump Interval-Learning for Individualized Decision Making

no code implementations17 Nov 2021 Hengrui Cai, Chengchun Shi, Rui Song, Wenbin Lu

To derive an optimal I2DR, our jump interval-learning method estimates the conditional mean of the outcome given the treatment and the covariates via jump penalized regression, and derives the corresponding optimal I2DR based on the estimated outcome regression function.

Decision Making regression

A Probit Tensor Factorization Model For Relational Learning

no code implementations6 Nov 2021 Ye Liu, Rui Song, Wenbin Lu, Yanghua Xiao

A large number of models and algorithms have been proposed to perform link prediction, among which tensor factorization method has proven to achieve state-of-the-art performance in terms of computation efficiency and prediction accuracy.

Knowledge Graphs Link Prediction +1

Doubly Robust Interval Estimation for Optimal Policy Evaluation in Online Learning

no code implementations29 Oct 2021 Ye Shen, Hengrui Cai, Rui Song

We use this probability to conduct valid inference on the online conditional mean estimator under each action and develop the doubly robust interval estimation (DREAM) method to infer the value under the estimated optimal policy in online learning.

valid

Label prompt for multi-label text classification

no code implementations18 Jun 2021 Rui Song, Xingbing Chen, Zelong Liu, Haining An, Zhiqi Zhang, Xiaoguang Wang, Hao Xu

In this paper, we propose a Label Mask multi-label text classification model (LM-MTC), which is inspired by the idea of cloze questions of language model.

Language Modelling Multi Label Text Classification +2

Periodic-GP: Learning Periodic World with Gaussian Process Bandits

no code implementations30 May 2021 Hengrui Cai, Zhihao Cen, Ling Leng, Rui Song

We consider the sequential decision optimization on the periodic environment, that occurs in a wide variety of real-world applications when the data involves seasonality, such as the daily demand of drivers in ride-sharing and dynamic traffic patterns in transportation.

Deeply-Debiased Off-Policy Interval Estimation

1 code implementation10 May 2021 Chengchun Shi, Runzhe Wan, Victor Chernozhukov, Rui Song

Off-policy evaluation learns a target policy's value with a historical dataset generated by a different behavior policy.

Off-policy evaluation

Calibrated Optimal Decision Making with Multiple Data Sources and Limited Outcome

1 code implementation21 Apr 2021 Hengrui Cai, Wenbin Lu, Rui Song

We consider the optimal decision-making problem in a primary sample of interest with multiple auxiliary sources available.

Decision Making

GEAR: On Optimal Decision Making with Auxiliary Data

no code implementations21 Apr 2021 Hengrui Cai, Rui Song, Wenbin Lu

We propose an auGmented inverse propensity weighted Experimental and Auxiliary sample-based decision Rule (GEAR) by maximizing the augmented inverse propensity weighted value estimator over a class of decision rules using the experimental sample, with the primary outcome being imputed based on the auxiliary sample.

Decision Making

ASFM-Net: Asymmetrical Siamese Feature Matching Network for Point Completion

1 code implementation19 Apr 2021 Yaqi Xia, Yan Xia, Wei Li, Rui Song, Kailang Cao, Uwe Stilla

We tackle the problem of object completion from point clouds and propose a novel point cloud completion network employing an Asymmetrical Siamese Feature Matching strategy, termed as ASFM-Net.

Point Cloud Completion

Topological Regularization for Graph Neural Networks Augmentation

no code implementations3 Apr 2021 Rui Song, Fausto Giunchiglia, Ke Zhao, Hao Xu

The complexity and non-Euclidean structure of graph data hinder the development of data augmentation methods similar to those in computer vision.

Data Augmentation Representation Learning

Giant Inverse Rashba-Edelstein Effect: Application to Monolayer OsBi$_2$

no code implementations11 Mar 2021 Rui Song, Ning Hao, Ping Zhang

We propose that the hybridization between two sets of Rashba bands can lead to an unconventional topology where the two Fermi circles from different bands own in-plane helical spin textures with the same chiralities, and possess group velocities with the same directions.

Materials Science Mesoscale and Nanoscale Physics

ANOCE: Analysis of Causal Effects with Multiple Mediators via Constrained Structural Learning

no code implementations ICLR 2021 Hengrui Cai, Rui Song, Wenbin Lu

Under a general causal graph, the exposure may have a direct effect on the outcome and also an indirect effect regulated by a set of mediators.

A REINFORCEMENT LEARNING FRAMEWORK FOR TIME DEPENDENT CAUSAL EFFECTS EVALUATION IN A/B TESTING

no code implementations1 Jan 2021 Chengchun Shi, Xiaoyu Wang, Shikai Luo, Rui Song, Hongtu Zhu, Jieping Ye

A/B testing, or online experiment is a standard business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries.

Reinforcement Learning (RL)

Counterfactual Fairness through Data Preprocessing

no code implementations1 Jan 2021 Haoyu Chen, Wenbin Lu, Rui Song, Pulak Ghosh

Machine learning has become more important in real-life decision-making but people are concerned about the ethical problems it may bring when used improperly.

BIG-bench Machine Learning counterfactual +2

GraphCGAN: Convolutional Graph Neural Network with Generative Adversarial Networks

no code implementations1 Jan 2021 Sheng Zhang, Rui Song, Wenbin Lu

In a number of experiments on benchmark datasets, we show that the proposed GraphCGAN outperforms the baseline methods by a significant margin.

Multi-direction Networks with Attentional Spectral Prior for Hyperspectral Image Classification

1 code implementation11 Dec 2020 Bobo Xi, Jiaojiao Li, Yunsong Li, Rui Song, Yuchao Xiao, Yanzi Shi, Qian Du.

Convolutional neural networks (CNNs) have achieved prominent progress in recent years and demonstrated remarkable properties in spectral-spatial hyperspectral image (HSI) classification.

General Classification Hyperspectral Image Classification

Statistical Inference for Online Decision Making via Stochastic Gradient Descent

1 code implementation14 Oct 2020 Haoyu Chen, Wenbin Lu, Rui Song

Focusing on the statistical inference of online decision making, we establish the asymptotic normality of the parameter estimator produced by our algorithm and the online inverse probability weighted value estimator we used to estimate the optimal value.

Decision Making

Statistical Inference for Online Decision-Making: In a Contextual Bandit Setting

no code implementations14 Oct 2020 Haoyu Chen, Wenbin Lu, Rui Song

Based on the properties of the parameter estimators, we further show that the in-sample inverse propensity weighted value estimator is asymptotically normal.

Decision Making

Multiscale Context-Aware Ensemble Deep KELM for Efficient Hyperspectral Image Classification

1 code implementation22 Sep 2020 Bobo Xi, Jiaojiao Li, Yunsong Li, Rui Song, Weiwei Sun, Qian Du.

Recently, multiscale spatial features have been widely utilized to improve the hyperspectral image (HSI) classification performance.

Computational Efficiency General Classification +1

Kernel Assisted Learning for Personalized Dose Finding

1 code implementation19 Jul 2020 Liangyu Zhu, Wenbin Lu, Michael R. Kosorok, Rui Song

In this article, we propose a kernel assisted learning method for estimating the optimal individualized dose rule.

Decision Making

Deep Prototypical Networks with Hybrid Residual Attention for Hyperspectral Image Classification

1 code implementation25 Jun 2020 Bobo Xi, Jiaojiao Li, Yunsong Li, Rui Song, Yanzi Shi, Songlin Liu, Qian Du

Recently, convolutional neural networks (CNNs) have attracted enormous attention in pattern recognition and demonstrated excellent performance in hyperspectral image (HSI) classification.

Classification General Classification +1

AdaptiveWeighted Attention Network with Camera Spectral Sensitivity Prior for Spectral Reconstruction from RGB Images

1 code implementation19 May 2020 Jiaojiao Li, Chaoxiong Wu, Rui Song, Yunsong Li, Fei Liu

Recent promising effort for spectral reconstruction (SR) focuses on learning a complicated mapping through using a deeper and wider convolutional neural networks (CNNs).

Spectral Reconstruction

Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework

1 code implementation5 Feb 2020 Chengchun Shi, Xiaoyu Wang, Shikai Luo, Hongtu Zhu, Jieping Ye, Rui Song

A/B testing, or online experiment is a standard business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries.

reinforcement-learning Reinforcement Learning (RL)

Neural network-based arithmetic coding of intra prediction modes in HEVC

no code implementations18 Sep 2017 Rui Song, Dong Liu, Houqiang Li, Feng Wu

In this paper, we propose an arithmetic coding strategy by training neural networks, and make preliminary studies on coding of the intra prediction modes in HEVC.

Multimedia

Robust Interpolation of Correspondences for Large Displacement Optical Flow

1 code implementation CVPR 2017 Yinlin Hu, Yunsong Li, Rui Song

In this paper, we present a Robust Interpolation method of Correspondences (called RicFlow) to overcome the weakness.

Optical Flow Estimation Superpixels

Efficient Coarse-To-Fine PatchMatch for Large Displacement Optical Flow

1 code implementation CVPR 2016 Yinlin Hu, Rui Song, Yunsong Li

Inspired by the nearest neighbor field (NNF) algorithms, our approach, called CPM (Coarse-to-fine PatchMatch), blends an efficient random search strategy with the coarse-to-fine scheme for optical flow problem.

Optical Flow Estimation

A ParaBoost Stereoscopic Image Quality Assessment (PBSIQA) System

no code implementations31 Mar 2016 Hyunsuk Ko, Rui Song, C. -C. Jay Kuo

The problem of stereoscopic image quality assessment, which finds applications in 3D visual content delivery such as 3DTV, is investigated in this work.

Stereoscopic image quality assessment

Robust Learning for Optimal Treatment Decision with NP-Dimensionality

no code implementations15 Oct 2015 Chengchun Shi, Rui Song, Wenbin Lu

In this paper, we propose a two-step estimation procedure for deriving the optimal treatment regime under NP dimensionality.

Sure Screening for Gaussian Graphical Models

2 code implementations29 Jul 2014 Shikai Luo, Rui Song, Daniela Witten

We propose {graphical sure screening}, or GRASS, a very simple and computationally-efficient screening procedure for recovering the structure of a Gaussian graphical model in the high-dimensional setting.

Sequential Advantage Selection for Optimal Treatment Regimes

no code implementations20 May 2014 Ailin Fan, Wenbin Lu, Rui Song

Gunter et al. (2011) proposed S-score which characterizes the magnitude of qualitative interaction of each variable with treatment individually.

Decision Making Variable Selection

MCL-3D: a database for stereoscopic image quality assessment using 2D-image-plus-depth source

no code implementations23 Mar 2014 Rui Song, Hyunsuk Ko, C. -C. Jay Kuo

A new stereoscopic image quality assessment database rendered using the 2D-image-plus-depth source, called MCL-3D, is described and the performance benchmarking of several known 2D and 3D image quality metrics using the MCL-3D database is presented in this work.

Benchmarking Stereoscopic image quality assessment

Structured Estimation in Nonparameteric Cox Model

no code implementations18 Jul 2012 Jelena Bradic, Rui Song

To better understand the interplay of censoring and sparsity we develop finite sample properties of nonparametric Cox proportional hazard's model.

Variable Selection

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