Search Results for author: Jin Liu

Found 29 papers, 6 papers with code

SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation

no code implementations10 Aug 2021 Xin Wang, Yasheng Wang, Fei Mi, Pingyi Zhou, Yao Wan, Xiao Liu, Li Li, Hao Wu, Jin Liu, Xin Jiang

Code representation learning, which aims to encode the semantics of source code into distributed vectors, plays an important role in recent deep-learning-based models for code intelligence.

Clone Detection Code Search +5

Effectiveness of Artificial Intelligence in Stock Market Prediction based on Machine Learning

no code implementations30 Jun 2021 Sohrab Mokhtari, Kang K. Yen, Jin Liu

This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies.

Stock Market Prediction

LI-Net: Large-Pose Identity-Preserving Face Reenactment Network

no code implementations7 Apr 2021 Jin Liu, Peng Chen, Tao Liang, Zhaoxing Li, Cai Yu, Shuqiao Zou, Jiao Dai, Jizhong Han

Face reenactment is a challenging task, as it is difficult to maintain accurate expression, pose and identity simultaneously.

Face Reenactment

DeepMI: Deep Multi-lead ECG Fusion for Identifying Myocardial Infarction and its Occurrence-time

no code implementations31 Mar 2021 Girmaw Abebe Tadesse, Hamza Javed, Yong liu, Jin Liu, Jiyan Chen, Komminist Weldemariam, Tingting Zhu

We propose an end-to-end deep learning approach, DeepMI, to classify MI from normal cases as well as identifying the time-occurrence of MI (defined as acute, recent and old), using a collection of fusion strategies on 12 ECG leads at data-, feature-, and decision-level.

Transfer Learning

Sufficient and Disentangled Representation Learning

no code implementations1 Jan 2021 Jian Huang, Yuling Jiao, Xu Liao, Jin Liu, Zhou Yu

We provide strong statistical guarantees for the learned representation by establishing an upper bound on the excess error of the objective function and show that it reaches the nonparametric minimax rate under mild conditions.

Representation Learning

Toward Understanding Supervised Representation Learning with RKHS and GAN

no code implementations1 Jan 2021 Xu Liao, Jin Liu, Tianwen Wen, Yuling Jiao, Jian Huang

At the population level, we formulate the ideal representation learning task as that of finding a nonlinear map that minimizes the sum of losses characterizing conditional independence (with RKHS) and disentanglement (with GAN).

Ranked #2 on Image Classification on STL-10 (using extra training data)

Image Classification Representation Learning

Multi-grained Trajectory Graph Convolutional Networks for Habit-unrelated Human Motion Prediction

no code implementations23 Dec 2020 Jin Liu, Jianqin Yin

A multi-grained trajectory graph convolutional networks based and lightweight framework is proposed for habit-unrelated human motion prediction.

Human motion prediction motion prediction

Quantum simulation of a three-mode optomechanical system based on the Fredkin-type interaction

no code implementations17 Dec 2020 Jin Liu, Yue-Hui Zhou, Jian Huang, Jin-Feng Huang, Jie-Qiao Liao

The realization of multimode optomechanical interactions in the single-photon strong-coupling regime is a desired task in cavity optomechanics, but it remains a challenge in realistic physical systems.

Quantum Physics

Generative Learning With Euler Particle Transport

no code implementations11 Dec 2020 Yuan Gao, Jian Huang, Yuling Jiao, Jin Liu, Xiliang Lu, Zhijian Yang

The key task in training is the estimation of the density ratios or differences that determine the residual maps.

A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment

no code implementations COLING 2020 Zhiwen Xie, Runjie Zhu, Kunsong Zhao, Jin Liu, Guangyou Zhou, Jimmy Xiangji Huang

In this paper, we propose a novel Contextual Alignment Enhanced Cross Graph Attention Network (CAECGAT) for the task of cross-lingual entity alignment, which is able to jointly learn the embeddings in different KGs by propagating cross-KG information through pre-aligned seed alignments.

Entity Alignment Graph Attention

ReInceptionE: Relation-Aware Inception Network with Joint Local-Global Structural Information for Knowledge Graph Embedding

no code implementations ACL 2020 Zhiwen Xie, Guangyou Zhou, Jin Liu, Jimmy Xiangji Huang

In this paper, we take the benefits of ConvE and KBGAT together and propose a Relation-aware Inception network with joint local-global structural information for knowledge graph Embedding (ReInceptionE).

Knowledge Graph Embedding

Deep Dimension Reduction for Supervised Representation Learning

1 code implementation10 Jun 2020 Jian Huang, Yuling Jiao, Xu Liao, Jin Liu, Zhou Yu

We propose a deep dimension reduction approach to learning representations with these characteristics.

Dimensionality Reduction Representation Learning

A Novel Recurrent Encoder-Decoder Structure for Large-Scale Multi-view Stereo Reconstruction from An Open Aerial Dataset

1 code implementation CVPR 2020 Jin Liu, Shunping Ji

In this paper, we present a synthetic aerial dataset, called the WHU dataset, we created for MVS tasks, which, to our knowledge, is the first large-scale multi-view aerial dataset.

Learning Implicit Generative Models with Theoretical Guarantees

no code implementations7 Feb 2020 Yuan Gao, Jian Huang, Yuling Jiao, Jin Liu

We then solve the McKean-Vlasov equation numerically using the forward Euler iteration, where the forward Euler map depends on the density ratio (density difference) between the distribution at current iteration and the underlying target distribution.

On Newton Screening

no code implementations27 Jan 2020 Jian Huang, Yuling Jiao, Lican Kang, Jin Liu, Yanyan Liu, Xiliang Lu, Yuanyuan Yang

Based on this KKT system, a built-in working set with a relatively small size is first determined using the sum of primal and dual variables generated from the previous iteration, then the primal variable is updated by solving a least-squares problem on the working set and the dual variable updated based on a closed-form expression.

Sparse Learning

A Support Detection and Root Finding Approach for Learning High-dimensional Generalized Linear Models

no code implementations16 Jan 2020 Jian Huang, Yuling Jiao, Lican Kang, Jin Liu, Yanyan Liu, Xiliang Lu

Feature selection is important for modeling high-dimensional data, where the number of variables can be much larger than the sample size.

Feature Selection

TrajectoryNet: a new spatio-temporal feature learning network for human motion prediction

no code implementations15 Oct 2019 Xiaoli Liu, Jianqin Yin, Jin Liu, Pengxiang Ding, Jun Liu, Huaping Liu

And the global temporal co-occurrence features represent the co-occurrence relationship that different subsequences in a complex motion sequence are appeared simultaneously, which can be obtained automatically with our proposed TrajectoryNet by reorganizing the temporal information as the depth dimension of the input tensor.

Human motion prediction motion prediction +1

Wasserstein-Wasserstein Auto-Encoders

no code implementations25 Feb 2019 Shunkang Zhang, Yuan Gao, Yuling Jiao, Jin Liu, Yang Wang, Can Yang

To address the challenges in learning deep generative models (e. g., the blurriness of variational auto-encoder and the instability of training generative adversarial networks, we propose a novel deep generative model, named Wasserstein-Wasserstein auto-encoders (WWAE).

BOLT-SSI: A Statistical Approach to Screening Interaction Effects for Ultra-High Dimensional Data

1 code implementation10 Feb 2019 Min Zhou, Mingwei Dai, Yuan YAO, Jin Liu, Can Yang, Heng Peng

In this paper, we first propose a simple method for sure screening interactions (SSI).

Methodology

6D Object Pose Estimation without PnP

no code implementations5 Feb 2019 Jin Liu, Sheng He

On this basis, a special layer, Collinear Equation Layer, is added next to region layer to output the 2D projections of the 3D bounding boxs corners.

6D Pose Estimation using RGB

6D Object Pose Estimation Based on 2D Bounding Box

no code implementations27 Jan 2019 Jin Liu, Sheng He

Our system trains a novel convolutional neural network to regress the unit quaternion, which represents the 3D rotation, from the partial image inside the bounding box returned by 2D detection systems.

6D Pose Estimation using RGB

BIVAS: A scalable Bayesian method for bi-level variable selection with applications

1 code implementation28 Mar 2018 Mingxuan Cai, Mingwei Dai, Jingsi Ming, Heng Peng, Jin Liu, Can Yang

To address this problem, we consider variational inference for bi-level variable selection (BIVAS).

Applications

A novel total variation model based on kernel functions and its application

no code implementations19 Nov 2017 Zhizheng Liang, Lei Zhang, Jin Liu, Yong Zhou

In this novel model, we first map each pixel value of an image into a Hilbert space by using a nonlinear map, and then define a coupled image of an original image in order to construct a kernel function.

Saliency Pattern Detection by Ranking Structured Trees

1 code implementation ICCV 2017 Lei Zhu, Haibin Ling, Jin Wu, Huiping Deng, Jin Liu

We show that the linear combination of structured labels can well model the saliency distribution in local regions.

RGB Salient Object Detection Saliency Prediction +1

What Makes it Difficult to Understand a Scientific Literature?

no code implementations4 Dec 2015 Mengyun Cao, Jiao Tian, Dezhi Cheng, Jin Liu, Xiaoping Sun

Through such analysis, we summarized some characteristics and problems which are reflected by people with different levels of knowledge on the comprehension of difficult science and technology literature, which can be modeled in semantic link network.

Reading Comprehension

Multi-ary Pulse Amplitude Modulated Signal Processing Using Bistable Stochastic Resonance

no code implementations International Conference on Noise and Fluctuations (ICNF) 2015 Jin Liu, Zan Li, Rui Gao, Jun Bai, Linlin Liang

On this basis, the mechanism of the BSR system response to MPAM signal inputs is elucidated, and a corresponding decoding scheme is proposed.

An adaptive Simulated Annealing-based satellite observation scheduling method combined with a dynamic task clustering strategy

no code implementations14 Jan 2014 Guohua Wu, Huilin Wang, Haifeng Li, Witold Pedrycz, Dishan Qiu, Manhao Ma, Jin Liu

In this study, we present an adaptive Simulated Annealing based scheduling algorithm aggregated with a dynamic task clustering strategy (or ASA-DTC for short) for satellite observation scheduling problems (SOSPs).

A Unified Primal Dual Active Set Algorithm for Nonconvex Sparse Recovery

no code implementations4 Oct 2013 Jian Huang, Yuling Jiao, Bangti Jin, Jin Liu, Xiliang Lu, Can Yang

In this paper, we consider the problem of recovering a sparse signal based on penalized least squares formulations.

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