Search Results for author: Xing Liu

Found 43 papers, 9 papers with code

Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations

no code implementations27 Feb 2024 Jiaqi Zhai, Lucy Liao, Xing Liu, Yueming Wang, Rui Li, Xuan Cao, Leon Gao, Zhaojie Gong, Fangda Gu, Michael He, Yinghai Lu, Yu Shi

Large-scale recommendation systems are characterized by their reliance on high cardinality, heterogeneous features and the need to handle tens of billions of user actions on a daily basis.

Recommendation Systems

GEA: Reconstructing Expressive 3D Gaussian Avatar from Monocular Video

no code implementations26 Feb 2024 Xinqi Liu, Chenming Wu, Xing Liu, Jialun Liu, Jinbo Wu, Chen Zhao, Haocheng Feng, Errui Ding, Jingdong Wang

This paper presents GEA, a novel method for creating expressive 3D avatars with high-fidelity reconstructions of body and hands based on 3D Gaussians.

Novel View Synthesis Pose Estimation

LEO Satellite and RIS: Two Keys to Seamless Indoor and Outdoor Localization

no code implementations28 Dec 2023 Pinjun Zheng, Xing Liu, Jiguang He, Gonzalo Seco-Granados, Tareq Y. Al-Naffouri

By leveraging the strong signal reception of the LEO satellite signals and capitalizing on the radio environment-reshaping capability of RISs, the integration of these two technologies presents a vision of a future where localization services transcend existing constraints.

Indoor Localization Outdoor Localization

Dynamic Fault Analysis in Substations Based on Knowledge Graphs

no code implementations22 Nov 2023 Weiwei Li, Xing Liu, Wei Wang, Lu Chen, Sizhe Li, Hui Fan

To address the challenge of identifying hidden danger in substations from unstructured text, a novel dynamic analysis method is proposed.

Knowledge Graphs

Tactile Active Inference Reinforcement Learning for Efficient Robotic Manipulation Skill Acquisition

no code implementations19 Nov 2023 Zihao Liu, Xing Liu, Yizhai Zhang, Zhengxiong Liu, Panfeng Huang

In this study, we propose a novel method for skill learning in robotic manipulation called Tactile Active Inference Reinforcement Learning (Tactile-AIRL), aimed at achieving efficient training.

reinforcement-learning Reinforcement Learning (RL)

Beamforming Design and Performance Evaluation for RIS-aided Localization using LEO Satellite Signals

no code implementations13 Sep 2023 Lei Wang, Pinjun Zheng, Xing Liu, Tarig Ballal, Tareq Y. Al-Naffouri

The growing availability of low-Earth orbit (LEO) satellites, coupled with the anticipated widespread deployment of reconfigurable intelligent surfaces (RISs), opens up promising prospects for new localization paradigms.

What are Public Concerns about ChatGPT? A Novel Self-Supervised Neural Topic Model Tells You

no code implementations4 Sep 2023 Rui Wang, Xing Liu, Yanan Wang, Haiping Huang

The recently released artificial intelligence conversational agent, ChatGPT, has gained significant attention in academia and real life.

Representation Learning

Attitude Determination in Urban Canyons: A Synergy between GNSS and 5G Observations

no code implementations22 Aug 2023 Pinjun Zheng, Xing Liu, Tarig Ballal, Tareq Y. Al-Naffouri

The tight fusion of the GNSS and the 5G observations results in a unique hybrid integer- and orthonormality-constrained optimization problem.

HD-Fusion: Detailed Text-to-3D Generation Leveraging Multiple Noise Estimation

no code implementations30 Jul 2023 Jinbo Wu, Xiaobo Gao, Xing Liu, Zhengyang Shen, Chen Zhao, Haocheng Feng, Jingtuo Liu, Errui Ding

In this paper, we study Text-to-3D content generation leveraging 2D diffusion priors to enhance the quality and detail of the generated 3D models.

Noise Estimation Text to 3D

Revisiting Neural Retrieval on Accelerators

no code implementations6 Jun 2023 Jiaqi Zhai, Zhaojie Gong, Yueming Wang, Xiao Sun, Zheng Yan, Fu Li, Xing Liu

A key component of retrieval is to model (user, item) similarity, which is commonly represented as the dot product of two learned embeddings.

Information Retrieval Retrieval

Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy

1 code implementation28 Apr 2023 Xing Liu, Andrew B. Duncan, Axel Gandy

Kernelized Stein discrepancy (KSD) is a score-based discrepancy widely used in goodness-of-fit tests.


MTrainS: Improving DLRM training efficiency using heterogeneous memories

no code implementations19 Apr 2023 Hiwot Tadese Kassa, Paul Johnson, Jason Akers, Mrinmoy Ghosh, Andrew Tulloch, Dheevatsa Mudigere, Jongsoo Park, Xing Liu, Ronald Dreslinski, Ehsan K. Ardestani

In Deep Learning Recommendation Models (DLRM), sparse features capturing categorical inputs through embedding tables are the major contributors to model size and require high memory bandwidth.

5G-Aided RTK Positioning in GNSS-Deprived Environments

no code implementations23 Mar 2023 Pinjun Zheng, Xing Liu, Tarig Ballal, Tareq Y. Al-Naffouri

This paper considers the localization problem in a 5G-aided global navigation satellite system (GNSS) based on real-time kinematic (RTK) technique.


A High-dimensional Convergence Theorem for U-statistics with Applications to Kernel-based Testing

no code implementations11 Feb 2023 Kevin H. Huang, Xing Liu, Andrew B. Duncan, Axel Gandy

We prove a convergence theorem for U-statistics of degree two, where the data dimension $d$ is allowed to scale with sample size $n$.


Learning the Network of Graphs for Graph Neural Networks

no code implementations8 Oct 2022 Yixiang Shan, Jielong Yang, Xing Liu, Yixing Gao, Hechang Chen, Shuzhi Sam Ge

Our model solves the first issue by simultaneously learning multiple relation graphs of data samples as well as a relation network of graphs, and solves the second and the third issue by selecting important data features as well as important data sample relations.

Relation Relation Network

SoccerNet 2022 Challenges Results

7 code implementations5 Oct 2022 Silvio Giancola, Anthony Cioppa, Adrien Deliège, Floriane Magera, Vladimir Somers, Le Kang, Xin Zhou, Olivier Barnich, Christophe De Vleeschouwer, Alexandre Alahi, Bernard Ghanem, Marc Van Droogenbroeck, Abdulrahman Darwish, Adrien Maglo, Albert Clapés, Andreas Luyts, Andrei Boiarov, Artur Xarles, Astrid Orcesi, Avijit Shah, Baoyu Fan, Bharath Comandur, Chen Chen, Chen Zhang, Chen Zhao, Chengzhi Lin, Cheuk-Yiu Chan, Chun Chuen Hui, Dengjie Li, Fan Yang, Fan Liang, Fang Da, Feng Yan, Fufu Yu, Guanshuo Wang, H. Anthony Chan, He Zhu, Hongwei Kan, Jiaming Chu, Jianming Hu, Jianyang Gu, Jin Chen, João V. B. Soares, Jonas Theiner, Jorge De Corte, José Henrique Brito, Jun Zhang, Junjie Li, Junwei Liang, Leqi Shen, Lin Ma, Lingchi Chen, Miguel Santos Marques, Mike Azatov, Nikita Kasatkin, Ning Wang, Qiong Jia, Quoc Cuong Pham, Ralph Ewerth, Ran Song, RenGang Li, Rikke Gade, Ruben Debien, Runze Zhang, Sangrok Lee, Sergio Escalera, Shan Jiang, Shigeyuki Odashima, Shimin Chen, Shoichi Masui, Shouhong Ding, Sin-wai Chan, Siyu Chen, Tallal El-Shabrawy, Tao He, Thomas B. Moeslund, Wan-Chi Siu, Wei zhang, Wei Li, Xiangwei Wang, Xiao Tan, Xiaochuan Li, Xiaolin Wei, Xiaoqing Ye, Xing Liu, Xinying Wang, Yandong Guo, YaQian Zhao, Yi Yu, YingYing Li, Yue He, Yujie Zhong, Zhenhua Guo, Zhiheng Li

The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.

Action Spotting Camera Calibration +3

Instantaneous GNSS Ambiguity Resolution and Attitude Determination via Riemannian Manifold Optimization

no code implementations20 May 2022 Xing Liu, Tarig Ballal, Mohanad Ahmed, Tareq Y. Al-Naffouri

Given the characteristics of the employed nonlinear constraints, we formulate GNSS attitude determination as an optimization problem on a manifold.

Grassmann Stein Variational Gradient Descent

1 code implementation7 Feb 2022 Xing Liu, Harrison Zhu, Jean-François Ton, George Wynne, Andrew Duncan

Stein variational gradient descent (SVGD) is a deterministic particle inference algorithm that provides an efficient alternative to Markov chain Monte Carlo.

Dimensionality Reduction

Machine Learning in Heterogeneous Porous Materials

no code implementations4 Feb 2022 Martha D'Eli, Hang Deng, Cedric Fraces, Krishna Garikipati, Lori Graham-Brady, Amanda Howard, Geoerge Karniadakid, Vahid Keshavarzzadeh, Robert M. Kirby, Nathan Kutz, Chunhui Li, Xing Liu, Hannah Lu, Pania Newell, Daniel O'Malley, Masa Prodanovic, Gowri Srinivasan, Alexandre Tartakovsky, Daniel M. Tartakovsky, Hamdi Tchelepi, Bozo Vazic, Hari Viswanathan, Hongkyu Yoon, Piotr Zarzycki

The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material sciences with experts in the areas of heterogeneous materials, machine learning (ML) and applied mathematics to identify how ML can advance materials research.

BIG-bench Machine Learning

Constrained Wrapped Least Squares: A Tool for High Accuracy GNSS Attitude Determination

no code implementations29 Dec 2021 Xing Liu, Tarig Ballal, Hui Chen, Tareq Y. Al-Naffouri

Attitude determination is a popular application of Global Navigation Satellite Systems (GNSS).

A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems

no code implementations NeurIPS 2021 Yi Ma, Xiaotian Hao, Jianye Hao, Jiawen Lu, Xing Liu, Tong Xialiang, Mingxuan Yuan, Zhigang Li, Jie Tang, Zhaopeng Meng

To address this problem, existing methods partition the overall DPDP into fixed-size sub-problems by caching online generated orders and solve each sub-problem, or on this basis to utilize the predicted future orders to optimize each sub-problem further.

Hierarchical Reinforcement Learning

Cross-Region Domain Adaptation for Class-level Alignment

no code implementations14 Sep 2021 Zhijie Wang, Xing Liu, Masanori Suganuma, Takayuki Okatani

To cope with this, we propose a method that applies adversarial training to align two feature distributions in the target domain.

Semantic Segmentation Synthetic-to-Real Translation +1

Matching in the Dark: A Dataset for Matching Image Pairs of Low-light Scenes

no code implementations ICCV 2021 Wenzheng Song, Masanori Suganuma, Xing Liu, Noriyuki Shimobayashi, Daisuke Maruta, Takayuki Okatani

To consider if and how well we can utilize such information stored in RAW-format images for image matching, we have created a new dataset named MID (matching in the dark).

TT-Rec: Tensor Train Compression for Deep Learning Recommendation Models

1 code implementation25 Jan 2021 Chunxing Yin, Bilge Acun, Xing Liu, Carole-Jean Wu

TT-Rec achieves 117 times and 112 times model size compression, for Kaggle and Terabyte, respectively.

Pushing the Envelope of Thin Crack Detection

no code implementations9 Jan 2021 Liang Xu, Taro Hatsutani, Xing Liu, Engkarat Techapanurak, Han Zou, Takayuki Okatani

We experimentally show that this makes it possible to detect cracks from an image of one-third the resolution of images used for annotation with about the same accuracy.

Bayesian Probabilistic Numerical Integration with Tree-Based Models

1 code implementation NeurIPS 2020 Harrison Zhu, Xing Liu, Ruya Kang, Zhichao Shen, Seth Flaxman, François-Xavier Briol

The advantages and disadvantages of this new methodology are highlighted on a set of benchmark tests including the Genz functions, and on a Bayesian survey design problem.

Numerical Integration

A New Dataset, Poisson GAN and AquaNet for Underwater Object Grabbing

no code implementations3 Mar 2020 Chongwei Liu, Zhihui Wang, Shijie Wang, Tao Tang, Yulong Tao, Caifei Yang, Haojie Li, Xing Liu, Xin Fan

We also propose a novel Poisson-blending Generative Adversarial Network (Poisson GAN) and an efficient object detection network (AquaNet) to address two common issues within related datasets: the class-imbalance problem and the problem of mass small object, respectively.

Generative Adversarial Network object-detection +1

2DR1-PCA and 2DL1-PCA: two variant 2DPCA algorithms based on none L2 norm

no code implementations23 Dec 2019 Xing Liu, Xiao-Jun Wu, Zi-Qi Li

In this paper, two novel methods: 2DR1-PCA and 2DL1-PCA are proposed for face recognition.

Face Recognition

A Compared Study Between Some Subspace Based Algorithms

no code implementations23 Dec 2019 Xing Liu, Xiao-Jun Wu, Zhen Liu, He-Feng Yin

The technology of face recognition has made some progress in recent years.

Face Recognition

Restoring Images with Unknown Degradation Factors by Recurrent Use of a Multi-branch Network

1 code implementation10 Jul 2019 Xing Liu, Masanori Suganuma, Xiyang Luo, Takayuki Okatani

The employment of convolutional neural networks has achieved unprecedented performance in the task of image restoration for a variety of degradation factors.

Deblurring JPEG Artifact Removal +2

Evaluating Artificial Systems for Pairwise Ranking Tasks Sensitive to Individual Differences

no code implementations30 May 2019 Xing Liu, Takayuki Okatani

There is another type of tasks for which what to predict is human perception itself, in which there are often individual differences.

Data Augmentation for Robust Keyword Spotting under Playback Interference

no code implementations1 Aug 2018 Anirudh Raju, Sankaran Panchapagesan, Xing Liu, Arindam Mandal, Nikko Strom

Accurate on-device keyword spotting (KWS) with low false accept and false reject rate is crucial to customer experience for far-field voice control of conversational agents.

Acoustic echo cancellation Data Augmentation +1

Feature Quantization for Defending Against Distortion of Images

no code implementations CVPR 2018 Zhun Sun, Mete Ozay, Yan Zhang, Xing Liu, Takayuki Okatani

In this work, we address the problem of improving robustness of convolutional neural networks (CNNs) to image distortion.


Integrating Deep Features for Material Recognition

no code implementations20 Nov 2015 Yan Zhang, Mete Ozay, Xing Liu, Takayuki Okatani

We propose a method for integration of features extracted using deep representations of Convolutional Neural Networks (CNNs) each of which is learned using a different image dataset of objects and materials for material recognition.

feature selection Material Recognition +1

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