Search Results for author: Rui Luo

Found 26 papers, 5 papers with code

Fréchet Statistics Based Change Point Detection in Multivariate Hawkes Process

no code implementations13 Aug 2023 Rui Luo, Vikram Krishnamurthy

This paper proposes a new approach for change point detection in causal networks of multivariate Hawkes processes using Frechet statistics.

Change Point Detection Point Processes

Semantic Embedded Deep Neural Network: A Generic Approach to Boost Multi-Label Image Classification Performance

no code implementations9 May 2023 Xin Shen, Xiaonan Zhao, Rui Luo

We compared the model performances among our approach, baseline approach, and 3 alternative approaches to leverage semantic features.

Attribute Classification +2

Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers

no code implementations11 Nov 2022 Peng Jia, Ruiqi Sun, Nan Li, Yu Song, Runyu Ning, Hongyan Wei, Rui Luo

We embed prior information of strongly lensed arcs at cluster-scale into the training data through simulation and then train the detection algorithm with simulated images.

DAGKT: Difficulty and Attempts Boosted Graph-based Knowledge Tracing

1 code implementation18 Oct 2022 Rui Luo, Fei Liu, Wenhao Liang, Yuhong Zhang, Chenyang Bu, Xuegang Hu

In the field of intelligent education, knowledge tracing (KT) has attracted increasing attention, which estimates and traces students' mastery of knowledge concepts to provide high-quality education.

Knowledge Tracing Question Similarity

Revisiting the Characteristics of Stochastic Gradient Noise and Dynamics

no code implementations20 Sep 2021 Yixin Wu, Rui Luo, Chen Zhang, Jun Wang, Yaodong Yang

In this paper, we characterize the noise of stochastic gradients and analyze the noise-induced dynamics during training deep neural networks by gradient-based optimizers.

More Than Just Attention: Improving Cross-Modal Attentions with Contrastive Constraints for Image-Text Matching

no code implementations20 May 2021 Yuxiao Chen, Jianbo Yuan, Long Zhao, Tianlang Chen, Rui Luo, Larry Davis, Dimitris N. Metaxas

Cross-modal attention mechanisms have been widely applied to the image-text matching task and have achieved remarkable improvements thanks to its capability of learning fine-grained relevance across different modalities.

Contrastive Learning Image Captioning +4

Affordance-Based Mobile Robot Navigation Among Movable Obstacles

no code implementations9 Feb 2021 Maozhen Wang, Rui Luo, Aykut Ozgun Onol, Taskin Padir

Avoiding obstacles in the perceived world has been the classical approach to autonomous mobile robot navigation.

Motion Planning Robot Navigation Robotics

Replica-Exchange Nos\'e-Hoover Dynamics for Bayesian Learning on Large Datasets

no code implementations NeurIPS 2020 Rui Luo, Qiang Zhang, Yaodong Yang, Jun Wang

In this paper, we present a new practical method for Bayesian learning that can rapidly draw representative samples from complex posterior distributions with multiple isolated modes in the presence of mini-batch noise.

Learning to Infer User Hidden States for Online Sequential Advertising

no code implementations3 Sep 2020 Zhaoqing Peng, Junqi Jin, Lan Luo, Yaodong Yang, Rui Luo, Jun Wang, Wei-Nan Zhang, Haiyang Xu, Miao Xu, Chuan Yu, Tiejian Luo, Han Li, Jian Xu, Kun Gai

To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both important.

On the FRB luminosity function -- II. Event rate density

1 code implementation10 Mar 2020 Rui Luo, Yunpeng Men, Kejia Lee, Weiyang Wang, D. R. Lorimer, Bing Zhang

Assuming a Schechter luminosity function form, we infer (at the 95% confidence level) that the characteristic FRB event rate density at the upper cut-off luminosity $L^*=2. 9_{-1. 7}^{+11. 9}\times10^{44}\,\rm erg\, s^{-1}$ is $\phi^*=339_{-313}^{+1074}\,\rm Gpc^{-3}\, yr^{-1}$, the power-law index is $\alpha=-1. 79_{-0. 35}^{+0. 31}$, and the lower cut-off luminosity is $L_0\le9. 1\times10^{41}\,\rm erg\, s^{-1}$.

High Energy Astrophysical Phenomena Cosmology and Nongalactic Astrophysics

A weakly supervised adaptive triplet loss for deep metric learning

no code implementations27 Sep 2019 Xiaonan Zhao, Huan Qi, Rui Luo, Larry Davis

We address the problem of distance metric learning in visual similarity search, defined as learning an image embedding model which projects images into Euclidean space where semantically and visually similar images are closer and dissimilar images are further from one another.

Metric Learning Retrieval +2

Wasserstein Robust Reinforcement Learning

no code implementations30 Jul 2019 Mohammed Amin Abdullah, Hang Ren, Haitham Bou Ammar, Vladimir Milenkovic, Rui Luo, Mingtian Zhang, Jun Wang

Reinforcement learning algorithms, though successful, tend to over-fit to training environments hampering their application to the real-world.

reinforcement-learning Reinforcement Learning (RL)

Automatic Radiology Report Generation based on Multi-view Image Fusion and Medical Concept Enrichment

no code implementations22 Jul 2019 Jianbo Yuan, Haofu Liao, Rui Luo, Jiebo Luo

In addition, in order to enrich the decoder with descriptive semantics and enforce the correctness of the deterministic medical-related contents such as mentions of organs or diagnoses, we extract medical concepts based on the radiology reports in the training data and fine-tune the encoder to extract the most frequent medical concepts from the x-ray images.

Descriptive Image Captioning +2

Replica-exchange Nosé-Hoover dynamics for Bayesian learning on large datasets

no code implementations29 May 2019 Rui Luo, Qiang Zhang, Yaodong Yang, Jun Wang

In this paper, we present a new practical method for Bayesian learning that can rapidly draw representative samples from complex posterior distributions with multiple isolated modes in the presence of mini-batch noise.

General Classification Image Classification +1

Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning

no code implementations26 Jan 2019 Ying Wen, Yaodong Yang, Rui Luo, Jun Wang

Though limited in real-world decision making, most multi-agent reinforcement learning (MARL) models assume perfectly rational agents -- a property hardly met due to individual's cognitive limitation and/or the tractability of the decision problem.

Decision Making Multi-agent Reinforcement Learning

Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning

no code implementations ICLR 2019 Ying Wen, Yaodong Yang, Rui Luo, Jun Wang, Wei Pan

Our methods are tested on both the matrix game and the differential game, which have a non-trivial equilibrium where common gradient-based methods fail to converge.

Multi-agent Reinforcement Learning reinforcement-learning +1

Parallel-tempered Stochastic Gradient Hamiltonian Monte Carlo for Approximate Multimodal Posterior Sampling

no code implementations4 Dec 2018 Rui Luo, Qiang Zhang, Yuanyuan Liu

We propose a new sampler that integrates the protocol of parallel tempering with the Nos\'e-Hoover (NH) dynamics.

Benchmarking Deep Sequential Models on Volatility Predictions for Financial Time Series

no code implementations8 Nov 2018 Qiang Zhang, Rui Luo, Yaodong Yang, Yuanyuan Liu

As an indicator of the level of risk or the degree of variation, volatility is important to analyse the financial market, and it is taken into consideration in various decision-making processes in financial activities.

Benchmarking Decision Making +2

On the normalised FRB luminosity function

2 code implementations29 Aug 2018 Rui Luo, Kejia Lee, Duncan R. Lorimer, Bing Zhang

In this paper, we measure the normalised luminosity function of FRBs.

High Energy Astrophysical Phenomena

Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning

1 code implementation NeurIPS 2018 Rui Luo, Jianhong Wang, Yaodong Yang, Zhanxing Zhu, Jun Wang

We propose a new sampling method, the thermostat-assisted continuously-tempered Hamiltonian Monte Carlo, for Bayesian learning on large datasets and multimodal distributions.

A Neural Stochastic Volatility Model

no code implementations30 Nov 2017 Rui Luo, Wei-Nan Zhang, Xiaojun Xu, Jun Wang

In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis and prediction in finance.

Time Series Time Series Analysis

Adversarial Variational Bayes Methods for Tweedie Compound Poisson Mixed Models

no code implementations16 Jun 2017 Yaodong Yang, Rui Luo, Yuanyuan Liu

Mixed models with random effects account for the covariance structure related to the grouping hierarchy in the data.

Variational Inference

Learning text representation using recurrent convolutional neural network with highway layers

no code implementations22 Jun 2016 Ying Wen, Wei-Nan Zhang, Rui Luo, Jun Wang

Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks.

Sentiment Analysis

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