Search Results for author: Zhen Zhang

Found 85 papers, 28 papers with code

HwTscSU’s Submissions on WAT 2022 Shared Task

no code implementations WAT 2022 Yilun Liu, Zhen Zhang, Shimin Tao, Junhui Li, Hao Yang

In this paper we describe our submission to the shared tasks of the 9th Workshop on Asian Translation (WAT 2022) on NICT–SAP under the team name ”HwTscSU”.

Domain Adaptation NMT +1

PCEE-BERT: Accelerating BERT Inference via Patient and Confident Early Exiting

1 code implementation Findings (NAACL) 2022 Zhen Zhang, Wei Zhu, Jinfan Zhang, Peng Wang, Rize Jin, Tae-Sun Chung

In this work, we propose Patient and Confident Early Exiting BERT (PCEE-BERT), an off-the-shelf sample-dependent early exiting method that can work with different PLMs and can also work along with popular model compression methods.

Model Compression

CLAP: Contrastive Learning with Augmented Prompts for Robustness on Pretrained Vision-Language Models

no code implementations28 Nov 2023 Yichao Cai, Yuhang Liu, Zhen Zhang, Javen Qinfeng Shi

By changing the style part of the text data, we empower the text encoder to emphasize latent content variables, ultimately enhancing the robustness of vision-language models.

Contrastive Learning Text Augmentation

Identifiable Latent Polynomial Causal Models Through the Lens of Change

no code implementations24 Oct 2023 Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton Van Den Hengel, Kun Zhang, Javen Qinfeng Shi

However, this progress rests on the assumption that the causal relationships among latent causal variables adhere strictly to linear Gaussian models.

Representation Learning

Interactive Navigation in Environments with Traversable Obstacles Using Large Language and Vision-Language Models

no code implementations13 Oct 2023 Zhen Zhang, Anran Lin, Chun Wai Wong, Xiangyu Chu, Qi Dou, K. W. Samuel Au

This paper proposes an interactive navigation framework by using large language and vision-language models, allowing robots to navigate in environments with traversable obstacles.

Language Modelling Large Language Model +1

ETGraph: A Pioneering Dataset Bridging Ethereum and Twitter

no code implementations2 Oct 2023 Qian Wang, Zhen Zhang, Zemin Liu, Shengliang Lu, Bingqiao Luo, Bingsheng He

While numerous public blockchain datasets are available, their utility is constrained by a singular focus on blockchain data.

Link Prediction

RADE: Reference-Assisted Dialogue Evaluation for Open-Domain Dialogue

no code implementations15 Sep 2023 Zhengliang Shi, Weiwei Sun, Shuo Zhang, Zhen Zhang, Pengjie Ren, Zhaochun Ren

To this end, we propose the Reference-Assisted Dialogue Evaluation (RADE) approach under the multi-task learning framework, which leverages the pre-created utterance as reference other than the gold response to relief the one-to-many problem.

Dialogue Evaluation Multi-Task Learning +1

Oobleck: Resilient Distributed Training of Large Models Using Pipeline Templates

1 code implementation15 Sep 2023 Insu Jang, Zhenning Yang, Zhen Zhang, Xin Jin, Mosharaf Chowdhury

Oobleck enables resilient distributed training of large DNN models with guaranteed fault tolerance.

CFDBench: A Comprehensive Benchmark for Machine Learning Methods in Fluid Dynamics

1 code implementation13 Sep 2023 Yining Luo, Yingfa Chen, Zhen Zhang

Compared to existing datasets, the advantages of CFDBench are (1) comprehensive.

Depth analysis of battery performance based on a data-driven approach

no code implementations30 Aug 2023 Zhen Zhang, Hongrui Sun, Hui Sun

Capacity attenuation is one of the most intractable issues in the current of application of the cells.

Factor Graph Neural Networks

no code implementations NeurIPS 2020 Zhen Zhang, Mohammed Haroon Dupty, Fan Wu, Javen Qinfeng Shi, Wee Sun Lee

In recent years, we have witnessed a surge of Graph Neural Networks (GNNs), most of which can learn powerful representations in an end-to-end fashion with great success in many real-world applications.

Representation Learning

Discovering a reaction-diffusion model for Alzheimer's disease by combining PINNs with symbolic regression

no code implementations16 Jul 2023 Zhen Zhang, Zongren Zou, Ellen Kuhl, George Em Karniadakis

Specifically, we integrate physics informed neural networks (PINNs) and symbolic regression to discover a reaction-diffusion type partial differential equation for tau protein misfolding and spreading.

regression Symbolic Regression

Certified Robustness for Large Language Models with Self-Denoising

1 code implementation14 Jul 2023 Zhen Zhang, Guanhua Zhang, Bairu Hou, Wenqi Fan, Qing Li, Sijia Liu, Yang Zhang, Shiyu Chang

This largely falls into the study of certified robust LLMs, i. e., all predictions of LLM are certified to be correct in a local region around the input.


OpenDelta: A Plug-and-play Library for Parameter-efficient Adaptation of Pre-trained Models

1 code implementation5 Jul 2023 Shengding Hu, Ning Ding, Weilin Zhao, Xingtai Lv, Zhen Zhang, Zhiyuan Liu, Maosong Sun

The scale of large pre-trained models (PTMs) poses significant challenges in adapting to downstream tasks due to the high optimization overhead and storage costs associated with full-parameter fine-tuning.

Uncertainty in Natural Language Processing: Sources, Quantification, and Applications

no code implementations5 Jun 2023 Mengting Hu, Zhen Zhang, Shiwan Zhao, Minlie Huang, Bingzhe Wu

Therefore, in this survey, we provide a comprehensive review of uncertainty-relevant works in the NLP field.

E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition

1 code implementation29 May 2023 Zhen Zhang, Mengting Hu, Shiwan Zhao, Minlie Huang, Haotian Wang, Lemao Liu, Zhirui Zhang, Zhe Liu, Bingzhe Wu

Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER systems in open environments.

named-entity-recognition Named Entity Recognition +1

RobustFair: Adversarial Evaluation through Fairness Confusion Directed Gradient Search

1 code implementation18 May 2023 Xuran Li, Peng Wu, Kaixiang Dong, Zhen Zhang, Yanting Chen

This matrix categorizes predictions as true fair, true biased, false fair, and false biased, and the perturbations guided by it can produce a dual impact on instances and their similar counterparts to either undermine prediction accuracy (robustness) or cause biased predictions (individual fairness).

Data Augmentation Fairness

Parameter-Efficient Cross-lingual Transfer of Vision and Language Models via Translation-based Alignment

1 code implementation2 May 2023 Zhen Zhang, Jialu Wang, Xin Eric Wang

Extensive experiments on XTD and Multi30K datasets, covering 11 languages under zero-shot, few-shot, and full-dataset learning scenarios, show that our framework significantly reduces the multilingual disparities among languages and improves cross-lingual transfer results, especially in low-resource scenarios, while only keeping and fine-tuning an extremely small number of parameters compared to the full model (e. g., Our framework only requires 0. 16\% additional parameters of a full-model for each language in the few-shot learning scenario).

Cross-Lingual Transfer Few-Shot Learning +1

A robust design of time-varying internal model principle-based control for ultra-precision tracking in a direct-drive servo stage

no code implementations13 Apr 2023 Yue Cao, Zhen Zhang

By means of the ESO feedback, the plant model is kept as nominal, and hence the structural robustness is achieved for the time-varying internal model.

Robust Design

BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection

1 code implementation29 Mar 2023 Sihao Hu, Zhen Zhang, Bingqiao Luo, Shengliang Lu, Bingsheng He, Ling Liu

As various forms of fraud proliferate on Ethereum, it is imperative to safeguard against these malicious activities to protect susceptible users from being victimized.

Fraud Detection

Multi-pooling 3D Convolutional Neural Network for fMRI Classification of Visual Brain States

no code implementations25 Mar 2023 Zhen Zhang, Masaki Takeda, Makoto Iwata

Neural decoding of visual object classification via functional magnetic resonance imaging (fMRI) data is challenging and is vital to understand underlying brain mechanisms.


Decoupled Model Schedule for Deep Learning Training

no code implementations16 Feb 2023 Hongzheng Chen, Cody Hao Yu, Shuai Zheng, Zhen Zhang, Zhiru Zhang, Yida Wang

Specifically, the schedule works on a PyTorch model and uses a set of schedule primitives to convert the model for common model training optimizations such as high-performance kernels, effective 3D parallelism, and efficient activation checkpointing.


Cost-minimization predictive energy management of a postal-delivery fuel cell electric vehicle with intelligent battery State-of-Charge Planner

no code implementations28 Dec 2022 Yang Zhou, Fuzeng Li, Xianfeng Xu, Zhen Zhang, Alexandre Ravey, Marie-Cécile Péra, Ruiqing Ma

Fuel cell electric vehicles have earned substantial attentions in recent decades due to their high-efficiency and zero-emission features, while the high operating costs remain the major barrier towards their large-scale commercialization.

energy management Management

Sparse Structure Search for Delta Tuning

1 code implementation NIPS 2022 Shengding Hu, Zhen Zhang, Ning Ding, Yadao Wang, Yasheng Wang, Zhiyuan Liu, Maosong Sun

Generally, DT methods exquisitely design delta modules (DT modules) which could be applied to arbitrary fine-grained positions inside PTMs.

Uncertainty Sentence Sampling by Virtual Adversarial Perturbation

no code implementations26 Oct 2022 Hanshan Zhang, Zhen Zhang, Hongfei Jiang, Yang song

Active learning for sentence understanding attempts to reduce the annotation cost by identifying the most informative examples.

Active Learning SST-2

How to Define the Propagation Environment Semantics and Its Application in Scatterer-Based Beam Prediction

no code implementations17 Sep 2022 Yutong Sun, Jianhua Zhang, Li Yu, Zhen Zhang, Ping Zhang

Inspired by task-oriented semantic communication and machine learning (ML) powered environment-channel mapping methods, this work aims to provide a new view of the environment from the semantic level, which defines the propagation environment semantics (PES) as a limited set of propagation environment semantic symbols (PESS) for diverse application tasks.

Truncated Matrix Power Iteration for Differentiable DAG Learning

1 code implementation30 Aug 2022 Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu, Ehsan M Abbasnejad, Mingming Gong, Kun Zhang, Javen Qinfeng Shi

Recovering underlying Directed Acyclic Graph (DAG) structures from observational data is highly challenging due to the combinatorial nature of the DAG-constrained optimization problem.

Identifying Weight-Variant Latent Causal Models

no code implementations30 Aug 2022 Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton Van Den Hengel, Kun Zhang, Javen Qinfeng Shi

The task of causal representation learning aims to uncover latent higher-level causal representations that affect lower-level observations.

Representation Learning

Identifying Latent Causal Content for Multi-Source Domain Adaptation

no code implementations30 Aug 2022 Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Kun Zhang, Javen Qinfeng Shi

This motivates us to propose a novel method for MSDA, which learns the invariant label distribution conditional on the latent content variable, instead of learning invariant representations.

Domain Adaptation

Sparse Structure Search for Parameter-Efficient Tuning

no code implementations15 Jun 2022 Shengding Hu, Zhen Zhang, Ning Ding, Yadao Wang, Yasheng Wang, Zhiyuan Liu, Maosong Sun

The searched structures preserve more than 99\% fine-tuning performance with 0. 01\% trainable parameters.

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

no code implementations25 May 2022 Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw, Aleš Leonardis, Radu Timofte, Zexin Zhang, Cen Liu, Yunbo Peng, Yue Lin, Gaocheng Yu, Jin Zhang, Zhe Ma, Hongbin Wang, Xiangyu Chen, Xintao Wang, Haiwei Wu, Lin Liu, Chao Dong, Jiantao Zhou, Qingsen Yan, Song Zhang, Weiye Chen, Yuhang Liu, Zhen Zhang, Yanning Zhang, Javen Qinfeng Shi, Dong Gong, Dan Zhu, Mengdi Sun, Guannan Chen, Yang Hu, Haowei Li, Baozhu Zou, Zhen Liu, Wenjie Lin, Ting Jiang, Chengzhi Jiang, Xinpeng Li, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Juan Marín-Vega, Michael Sloth, Peter Schneider-Kamp, Richard Röttger, Chunyang Li, Long Bao, Gang He, Ziyao Xu, Li Xu, Gen Zhan, Ming Sun, Xing Wen, Junlin Li, Shuang Feng, Fei Lei, Rui Liu, Junxiang Ruan, Tianhong Dai, Wei Li, Zhan Lu, Hengyan Liu, Peian Huang, Guangyu Ren, Yonglin Luo, Chang Liu, Qiang Tu, Fangya Li, Ruipeng Gang, Chenghua Li, Jinjing Li, Sai Ma, Chenming Liu, Yizhen Cao, Steven Tel, Barthelemy Heyrman, Dominique Ginhac, Chul Lee, Gahyeon Kim, Seonghyun Park, An Gia Vien, Truong Thanh Nhat Mai, Howoon Yoon, Tu Vo, Alexander Holston, Sheir Zaheer, Chan Y. Park

The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).

Image Restoration Vocal Bursts Intensity Prediction

MolMiner: You only look once for chemical structure recognition

no code implementations23 May 2022 Youjun Xu, Jinchuan Xiao, Chia-Han Chou, Jianhang Zhang, Jintao Zhu, Qiwan Hu, Hemin Li, Ningsheng Han, Bingyu Liu, Shuaipeng Zhang, Jinyu Han, Zhen Zhang, Shuhao Zhang, Weilin Zhang, Luhua Lai, Jianfeng Pei

Due to a backlog of decades and an increasing amount of these printed literature, there is a high demand for the translation of printed depictions into machine-readable formats, which is known as Optical Chemical Structure Recognition (OCSR).

object-detection Object Detection +1

Adversarial Training-Aided Time-Varying Channel Prediction for TDD/FDD Systems

no code implementations25 Apr 2022 Zhen Zhang, Yuxiang Zhang, Jianhua Zhang, Feifei Gao

In this paper, a time-varying channel prediction method based on conditional generative adversarial network (CPcGAN) is proposed for time division duplexing/frequency division duplexing (TDD/FDD) systems.

Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump

1 code implementation21 Apr 2022 Sihao Hu, Zhen Zhang, Shengliang Lu, Bingsheng He, Zhao Li

With the proliferation of pump-and-dump schemes (P&Ds) in the cryptocurrency market, it becomes imperative to detect such fraudulent activities in advance to alert potentially susceptible investors.

Enhanced Contour Tracking: a Time-Varying Internal Model Principle-Based Approach

no code implementations23 Mar 2022 Yue Cao, Zhen Zhang

The proposed TV-IMCC is twofold, including an extended position domain framework with master-slave structures for contour regulation, and a time-varying internal model principle-based controller for each axial tracking precision improvement.

Systems Biology: Identifiability analysis and parameter identification via systems-biology informed neural networks

2 code implementations3 Feb 2022 Mitchell Daneker, Zhen Zhang, George Em Karniadakis, Lu Lu

The dynamics of systems biological processes are usually modeled by a system of ordinary differential equations (ODEs) with many unknown parameters that need to be inferred from noisy and sparse measurements.

SympOCnet: Solving optimal control problems with applications to high-dimensional multi-agent path planning problems

1 code implementation14 Jan 2022 Tingwei Meng, Zhen Zhang, Jérôme Darbon, George Em Karniadakis

Solving high-dimensional optimal control problems in real-time is an important but challenging problem, with applications to multi-agent path planning problems, which have drawn increased attention given the growing popularity of drones in recent years.

GFINNs: GENERIC Formalism Informed Neural Networks for Deterministic and Stochastic Dynamical Systems

no code implementations31 Aug 2021 Zhen Zhang, Yeonjong Shin, George Em Karniadakis

We propose the GENERIC formalism informed neural networks (GFINNs) that obey the symmetric degeneracy conditions of the GENERIC formalism.

Adaptive Optimizers with Sparse Group Lasso for Neural Networks in CTR Prediction

1 code implementation30 Jul 2021 Yun Yue, Yongchao Liu, Suo Tong, Minghao Li, Zhen Zhang, Chunyang Wen, Huanjun Bao, Lihong Gu, Jinjie Gu, Yixiang Mu

We develop a novel framework that adds the regularizers of the sparse group lasso to a family of adaptive optimizers in deep learning, such as Momentum, Adagrad, Adam, AMSGrad, AdaHessian, and create a new class of optimizers, which are named Group Momentum, Group Adagrad, Group Adam, Group AMSGrad and Group AdaHessian, etc., accordingly.

Click-Through Rate Prediction

Uncertainty-Guided Mixup for Semi-Supervised Domain Adaptation without Source Data

no code implementations14 Jul 2021 Ning Ma, Jiajun Bu, Zhen Zhang, Sheng Zhou

Present domain adaptation methods usually perform explicit representation alignment by simultaneously accessing the source data and target data.

Privacy Preserving Semi-supervised Domain Adaptation +1

Semi-Supervised Hypothesis Transfer for Source-Free Domain Adaptation

no code implementations14 Jul 2021 Ning Ma, Jiajun Bu, Lixian Lu, Jun Wen, Zhen Zhang, Sheng Zhou, Xifeng Yan

Domain Adaptation has been widely used to deal with the distribution shift in vision, language, multimedia etc.

Source-Free Domain Adaptation

Adaptive Optimizers with Sparse Group Lasso

no code implementations1 Jan 2021 Yun Yue, Suo Tong, Zhen Zhang, Yongchao Liu, Chunyang Wen, Huanjun Bao, Jinjie Gu, Yixiang Mu

We develop a novel framework that adds the regularizers to a family of adaptive optimizers in deep learning, such as MOMENTUM, ADAGRAD, ADAM, AMSGRAD, ADAHESSIAN, and create a new class of optimizers, which are named GROUP MOMENTUM, GROUP ADAGRAD, GROUP ADAM, GROUP AMSGRAD and GROUP ADAHESSIAN, etc., accordingly.

Market Impact in Trader-Agents: Adding Multi-Level Order-Flow Imbalance-Sensitivity to Automated Trading Systems

no code implementations23 Dec 2020 Zhen Zhang, Dave Cliff

We demonstrate that the new imbalance-sensitive trader-agents introduced here do exhibit market impact effects, and hence are better-suited to operating in markets where impact is a factor of concern or interest, but do not suffer the weaknesses of the methods used by Church & Cliff.

Learning Poisson systems and trajectories of autonomous systems via Poisson neural networks

1 code implementation5 Dec 2020 Pengzhan Jin, Zhen Zhang, Ioannis G. Kevrekidis, George Em Karniadakis

We propose the Poisson neural networks (PNNs) to learn Poisson systems and trajectories of autonomous systems from data.

Cyclic Label Propagation for Graph Semi-supervised Learning

no code implementations24 Nov 2020 Zhao Li, Yixin Liu, Zhen Zhang, Shirui Pan, Jianliang Gao, Jiajun Bu

To overcome these limitations, we introduce a novel framework for graph semi-supervised learning termed as Cyclic Label Propagation (CycProp for abbreviation), which integrates GNNs into the process of label propagation in a cyclic and mutually reinforcing manner to exploit the advantages of both GNNs and LPA.

Node Classification

Deep Reinforcement Learning of Transition States

no code implementations13 Nov 2020 Jun Zhang, Yao-Kun Lei, Zhen Zhang, Xu Han, Maodong Li, Lijiang Yang, Yi Isaac Yang, Yi Qin Gao

Combining reinforcement learning (RL) and molecular dynamics (MD) simulations, we propose a machine-learning approach (RL$^\ddag$) to automatically unravel chemical reaction mechanisms.

reinforcement-learning Reinforcement Learning (RL)

CL-MAPF: Multi-Agent Path Finding for Car-Like Robots with Kinematic and Spatiotemporal Constraints

1 code implementation1 Nov 2020 Licheng Wen, Zhen Zhang, Zhe Chen, Xiangrui Zhao, Yong liu

In this paper, we give a mathematical formalization of Multi-Agent Path Finding for Car-Like robots (CL-MAPF) problem.

Robotics Multiagent Systems

Brain Tumor Segmentation Network Using Attention-based Fusion and Spatial Relationship Constraint

no code implementations29 Oct 2020 Chenyu Liu, Wangbin Ding, Lei LI, Zhen Zhang, Chenhao Pei, Liqin Huang, Xiahai Zhuang

Considering that multi-modal MR images can reflect different tumor biological properties, we develop a novel multi-modal tumor segmentation network (MMTSN) to robustly segment brain tumors based on multi-modal MR images.

Brain Tumor Segmentation Tumor Segmentation

Kagome quantum anomalous Hall effect with high Chern number and large band gap

no code implementations15 Oct 2020 Zhen Zhang, Jing-Yang You, Xing-Yu Ma, Bo Gu, Gang Su

For the bilayer compound Co6Sn5Se4, it becomes a half-metal, with a relatively flat plateau in its anomalous Hall conductivity corresponding to |C| = 3 near the Fermi level.

Materials Science

Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images

1 code implementation13 Aug 2020 Zhen Zhang, Chenyu Liu, Wangbin Ding, Sihan Wang, Chenhao Pei, Mingjing Yang, Liqin Huang

The PRSN is designed to segment pathological region based on the result of ASSN, in which a fusion block based on channel attention is proposed to better aggregate multi-modality information from multi-modality CMR images.

Denoising Segmentation

Is Network the Bottleneck of Distributed Training?

1 code implementation17 Jun 2020 Zhen Zhang, Chaokun Chang, Haibin Lin, Yida Wang, Raman Arora, Xin Jin

As such, we advocate that the real challenge of distributed training is for the network community to develop high-performance network transport to fully utilize the network capacity and achieve linear scale-out.

Time-Stretched Femtosecond Lidar Using Microwave Photonic Signal Processing

no code implementations29 May 2020 Lijie Zhao, Haiyun Xia, Yihua Hu, Tengfei Wu, Zhen Zhang, Jibo Han, Yunbin Wu, Tiancheng Luo

After that, the frequency variation of the microwave pulse is uploaded to the first order sidebands.

A Perspective on Deep Learning for Molecular Modeling and Simulations

no code implementations25 Apr 2020 Jun Zhang, Yao-Kun Lei, Zhen Zhang, Junhan Chang, Maodong Li, Xu Han, Lijiang Yang, Yi Isaac Yang, Yi Qin Gao

Deep learning is transforming many areas in science, and it has great potential in modeling molecular systems.

Multiplicative Gaussian Particle Filter

no code implementations29 Feb 2020 Xuan Su, Wee Sun Lee, Zhen Zhang

We propose a new sampling-based approach for approximate inference in filtering problems.

SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems

1 code implementation11 Jan 2020 Pengzhan Jin, Zhen Zhang, Aiqing Zhu, Yifa Tang, George Em. Karniadakis

We propose new symplectic networks (SympNets) for identifying Hamiltonian systems from data based on a composition of linear, activation and gradient modules.

KerGM: Kernelized Graph Matching

1 code implementation NeurIPS 2019 Zhen Zhang, Yijian Xiang, Lingfei Wu, Bing Xue, Arye Nehorai

Graph matching plays a central role in such fields as computer vision, pattern recognition, and bioinformatics.

Graph Matching

Visual Relationship Detection with Low Rank Non-Negative Tensor Decomposition

no code implementations22 Nov 2019 Mohammed Haroon Dupty, Zhen Zhang, Wee Sun Lee

We address the problem of Visual Relationship Detection (VRD) which aims to describe the relationships between pairs of objects in the form of triplets of (subject, predicate, object).

Relationship Detection Tensor Decomposition +1

Hierarchical Graph Pooling with Structure Learning

3 code implementations14 Nov 2019 Zhen Zhang, Jiajun Bu, Martin Ester, Jianfeng Zhang, Chengwei Yao, Zhi Yu, Can Wang

HGP-SL incorporates graph pooling and structure learning into a unified module to generate hierarchical representations of graphs.

Graph Classification Representation Learning

Learning Clustered Representation for Complex Free Energy Landscapes

no code implementations7 Jun 2019 Jun Zhang, Yao-Kun Lei, Xing Che, Zhen Zhang, Yi Isaac Yang, Yi Qin Gao

In this paper we first analyzed the inductive bias underlying the data scattered across complex free energy landscapes (FEL), and exploited it to train deep neural networks which yield reduced and clustered representation for the FEL.

Clustering Dimensionality Reduction +1

Factor Graph Neural Network

1 code implementation3 Jun 2019 Zhen Zhang, Fan Wu, Wee Sun Lee

Most of the successful deep neural network architectures are structured, often consisting of elements like convolutional neural networks and gated recurrent neural networks.

phq: a Fortran code to compute phonon quasiparticle properties and dispersions

1 code implementation18 Feb 2019 Zhen Zhang, Dong-Bo Zhang, Tao Sun, Renata Wentzcovitch

We here introduce a Fortran code that computes anharmonic free energy of solids from first-principles based on our phonon quasiparticle approach.

Materials Science

Aligning Infinite-Dimensional Covariance Matrices in Reproducing Kernel Hilbert Spaces for Domain Adaptation

no code implementations CVPR 2018 Zhen Zhang, Mianzhi Wang, Yan Huang, Arye Nehorai

Domain shift, which occurs when there is a mismatch between the distributions of training (source) and testing (target) datasets, usually results in poor performance of the trained model on the target domain.

Domain Adaptation

OmicsMapNet: Transforming omics data to take advantage of Deep Convolutional Neural Network for discovery

no code implementations14 Apr 2018 Shiyong Ma, Zhen Zhang

We developed OmicsMapNet approach to take advantage of existing deep leaning frameworks to analyze high-dimensional omics data as 2-dimensional images.

Learning Deep Gradient Descent Optimization for Image Deconvolution

1 code implementation10 Apr 2018 Dong Gong, Zhen Zhang, Qinfeng Shi, Anton Van Den Hengel, Chunhua Shen, Yanning Zhang

Extensive experiments on synthetic benchmarks and challenging real-world images demonstrate that the proposed deep optimization method is effective and robust to produce favorable results as well as practical for real-world image deblurring applications.

Blind Image Deblurring Image Deblurring +1

Depth and Image Restoration From Light Field in a Scattering Medium

no code implementations ICCV 2017 Jiandong Tian, Zachary Murez, Tong Cui, Zhen Zhang, David Kriegman, Ravi Ramamoorthi

First, we present a new single image restoration algorithm which removes backscatter and attenuation from images better than existing methods, and apply it to each view in the light field.

Depth Estimation Image Restoration

Joint Probabilistic Matching Using m-Best Solutions

no code implementations CVPR 2016 Seyed Hamid Rezatofighi, Anton Milan, Zhen Zhang, Qinfeng Shi, Anthony Dick, Ian Reid

Matching between two sets of objects is typically approached by finding the object pairs that collectively maximize the joint matching score.

Person Re-Identification

Joint Probabilistic Data Association Revisited

no code implementations ICCV 2015 Seyed Hamid Rezatofighi, Anton Milan, Zhen Zhang, Qinfeng Shi, Anthony Dick, Ian Reid

In this paper, we revisit the joint probabilistic data association (JPDA) technique and propose a novel solution based on recent developments in finding the m-best solutions to an integer linear program.

Constraint Reduction using Marginal Polytope Diagrams for MAP LP Relaxations

no code implementations17 Dec 2013 Zhen Zhang, Qinfeng Shi, Yanning Zhang, Chunhua Shen, Anton Van Den Hengel

We show that using Marginal Polytope Diagrams allows the number of constraints to be reduced without loosening the LP relaxations.

A feasible roadmap for unsupervised deconvolution of two-source mixed gene expressions

no code implementations25 Oct 2013 Niya Wang, Eric P. Hoffman, Robert Clarke, Zhen Zhang, David M. Herrington, Ie-Ming Shih, Douglas A. Levine, Guoqiang Yu, Jianhua Xuan, Yue Wang

Tissue heterogeneity is a major confounding factor in studying individual populations that cannot be resolved directly by global profiling.

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