Search Results for author: Chang Liu

Found 162 papers, 45 papers with code

面向人工智能伦理计算的中文道德词典构建方法研究(Construction of a Chinese Moral Dictionary for Artificial Intelligence Ethical Computing)

no code implementations CCL 2020 Hongrui Wang, Chang Liu, Dong Yu

道德词典资源的建设是人工智能伦理计算的一个研究重点。由于道德行为复杂多样, 现有的英文道德词典分类体系并不完善, 而中文方面目前尚未有相关的词典资源, 理论体系和构建方法仍待探究。针对以上问题, 该文提出了面向人工智能伦理计算的中文道德词典构建任务, 设计了四类标签和四种类型, 得到包含25, 012个词的中文道德词典资源。实验结果表明, 该词典资源不仅能够使机器学会道德知识, 判断词的道德标签和类型, 而且能够为句子级别的道德文本分析提供数据支持。

Variance Reduction and Quasi-Newton for Particle-Based Variational Inference

no code implementations ICML 2020 Michael Zhu, Chang Liu, Jun Zhu

Particle-based Variational Inference methods (ParVIs), like Stein Variational Gradient Descent, are nonparametric variational inference methods that optimize a set of particles to best approximate a target distribution.

Bayesian Inference Riemannian optimization +1

基于跨语言双语预训练及Bi-LSTM的汉-越平行句对抽取方法(Chinese-Vietnamese Parallel Sentence Pair Extraction Method Based on Cross-lingual Bilingual Pre-training and Bi-LSTM)

no code implementations CCL 2020 Chang Liu, Shengxiang Gao, Zhengtao Yu, Yuxin Huang, Congcong You

汉越平行句对抽取是缓解汉越平行语料库数据稀缺的重要方法。平行句对抽取可转换为同一语义空间下的句子相似性分类任务, 其核心在于双语语义空间对齐。传统语义空间对齐方法依赖于大规模的双语平行语料, 越南语作为低资源语言获取大规模平行语料相对困难。针对这个问题本文提出一种利用种子词典进行跨语言双语预训练及Bi-LSTM(Bi-directional Long Short-Term Memory)的汉-越平行句对抽取方法。预训练中仅需要大量的汉越单语和一个汉越种子词典, 通过利用汉越种子词典将汉越双语映射到公共语义空间进行词对齐。再利用Bi-LSTM和CNN(Convolutional Neural Networks)分别提取句子的全局特征和局部特征从而最大化表示汉-越句对之间的语义相关性。实验结果表明, 本文模型在F1得分上提升7. 1%, 优于基线模型。

Recovering Latent Causal Factor for Generalization to Distributional Shifts

1 code implementation NeurIPS 2021 Xinwei Sun, Botong Wu, Xiangyu Zheng, Chang Liu, Wei Chen, Tao Qin, Tie-Yan Liu

To avoid such a spurious correlation, we propose \textbf{La}tent \textbf{C}ausal \textbf{I}nvariance \textbf{M}odels (LaCIM) that specifies the underlying causal structure of the data and the source of distributional shifts, guiding us to pursue only causal factor for prediction.

Feature-Gate Coupling for Dynamic Network Pruning

1 code implementation29 Nov 2021 Mengnan Shi, Chang Liu, Qixiang Ye, Jianbin Jiao

Gating modules have been widely explored in dynamic network pruning to reduce the run-time computational cost of deep neural networks while preserving the representation of features.

Contrastive Learning Network Pruning

Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification

no code implementations20 Nov 2021 Long Gao, Chang Liu, Dooman Arefan, Ashok Panigrahy, Margarita L. Zuley, Shandong Wu

To address this challenge, we propose a medical-knowledge-guided one-class classification approach that leverages domain-specific knowledge of classification tasks to boost the model's performance.

Classification Image Classification

Constrained Deep One-Class Feature Learning For Classifying Imbalanced Medical Images

no code implementations20 Nov 2021 Long Gao, Chang Liu, Dooman Arefan, Ashok Panigrahy, Shandong Wu

These methods mainly focus on capturing either compact or descriptive features, where the information of the samples of a given one class is not sufficiently utilized.

Towards Generating Real-World Time Series Data

no code implementations16 Nov 2021 Hengzhi Pei, Kan Ren, Yuqing Yang, Chang Liu, Tao Qin, Dongsheng Li

In this paper, we propose a novel generative framework for RTS data - RTSGAN to tackle the aforementioned challenges.

Time Series

Decentralized On-Ramp Merging Control of Connected and Automated Vehicles in the Mixed Traffic Using Control Barrier Functions

no code implementations1 Nov 2021 Haoji Liu, Weichao Zhuang, Guodong Yin, Rongcan Li, Chang Liu, Shanxing Zhou

We first formulate the optimal merging control problem, which includes the constraints of safety and vehicle dynamics, with the objectives of minimizing travel time and energy consumption.

Improving Location Recommendation with Urban Knowledge Graph

no code implementations1 Nov 2021 Chang Liu, Chen Gao, Depeng Jin, Yong Li

We first conduct information propagation on two sub-graphs to learn the representations of POIs and users.

Spatial-temporal water area monitoring of Miyun Reservoir using remote sensing imagery from 1984 to 2020

no code implementations14 Oct 2021 Chang Liu, Hairong Tang, Luyan Ji, Yongchao Zhao

Based on the mapping results, we analyzed the changes of Miyun Reservoir from 1984 to 2020 and the driving factors of them.

Motivating Effort with Information about Future Rewards

no code implementations11 Oct 2021 Chang Liu

The principal knows the reward of the task and provides information to the agent over time.

Building an Efficient and Effective Retrieval-based Dialogue System via Mutual Learning

no code implementations1 Oct 2021 Chongyang Tao, Jiazhan Feng, Chang Liu, Juntao Li, Xiubo Geng, Daxin Jiang

For this task, the adoption of pre-trained language models (such as BERT) has led to remarkable progress in a number of benchmarks.


You Cannot Easily Catch Me: A Low-Detectable Adversarial Patch for Object Detectors

no code implementations30 Sep 2021 Zijian Zhu, Hang Su, Chang Liu, Wenzhao Xiang, Shibao Zheng

Fortunately, most existing adversarial patches can be outwitted, disabled and rejected by a simple classification network called an adversarial patch detector, which distinguishes adversarial patches from original images.

Self-Driving Cars

Learning to Learn across Diverse Data Biases in Deep Face Recognition

no code implementations29 Sep 2021 Chang Liu, Xiang Yu, Yi-Hsuan Tsai, Ramin Moslemi, Masoud Faraki, Manmohan Chandraker, Yun Fu

Convolutional Neural Networks have achieved remarkable success in face recognition, in part due to the abundant availability of data.

Face Recognition Meta-Learning

Improving Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator

no code implementations13 Sep 2021 Wenzhao Xiang, Hang Su, Chang Liu, Yandong Guo, Shibao Zheng

Adversarial examples can deceive a deep neural network (DNN) by significantly altering its response with imperceptible perturbations, which poses new potential vulnerabilities as the growing ubiquity of DNNs.

Adversarial Attack Affine Transformation

Learning-based Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks

no code implementations26 Aug 2021 Chang Liu, Weijie Yuan, Shuangyang Li, Xuemeng Liu, Derrick Wing Kwan Ng, Yonghui Li

Then, by exploiting the penalty method, a versatile unsupervised DL-based predictive beamforming design framework is developed to address the formulated design problem.

Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE

no code implementations ICML Workshop AML 2021 Wenzhao Xiang, Chang Liu, Shibao Zheng

Traditional adversarial examples are typically generated by adding perturbation noise to the input image within a small matrix norm.

Adversarial Attack

Vision-Language Transformer and Query Generation for Referring Segmentation

1 code implementation ICCV 2021 Henghui Ding, Chang Liu, Suchen Wang, Xudong Jiang

We introduce transformer and multi-head attention to build a network with an encoder-decoder attention mechanism architecture that "queries" the given image with the language expression.

Referring Expression Segmentation

Noise-Resistant Deep Metric Learning with Probabilistic Instance Filtering

no code implementations3 Aug 2021 Chang Liu, Han Yu, Boyang Li, Zhiqi Shen, Zhanning Gao, Peiran Ren, Xuansong Xie, Lizhen Cui, Chunyan Miao

Noisy labels are commonly found in real-world data, which cause performance degradation of deep neural networks.

Metric Learning

Tracing Halpha Fibrils through Bayesian Deep Learning

no code implementations16 Jul 2021 Haodi Jiang, Ju Jing, Jiasheng Wang, Chang Liu, Qin Li, Yan Xu, Jason T. L. Wang, Haimin Wang

Our method consists of a data pre-processing component that prepares training data from a threshold-based tool, a deep learning model implemented as a Bayesian convolutional neural network for probabilistic image segmentation with uncertainty quantification to predict fibrils, and a post-processing component containing a fibril-fitting algorithm to determine fibril orientations.

Semantic Segmentation

On the Generative Utility of Cyclic Conditionals

1 code implementation NeurIPS 2021 Chang Liu, Haoyue Tang, Tao Qin, Jintao Wang, Tie-Yan Liu

This is motivated by the observation that deep generative models, in addition to a likelihood model $p(x|z)$, often also use an inference model $q(z|x)$ for extracting representation, but they rely on a usually uninformative prior distribution $p(z)$ to define a joint distribution, which may render problems like posterior collapse and manifold mismatch.

Human-in-the-loop model explanation via verbatim boundary identification in generated neighborhoods

1 code implementation24 Jun 2021 Xianlong Zeng, Fanghao Song, Zhongen Li, Krerkkiat Chusap, Chang Liu

Our method can be divided into three stages: 1) a neighborhood generation stage, which generates instances based on the given sample; 2) a classification stage, which yields classifications on the generated instances to carve out the local decision boundary and delineate the model behavior; and 3) a human-in-the-loop stage, which involves human to refine and explore the neighborhood of interest.

Explainable artificial intelligence

Pre-training transformer-based framework on large-scale pediatric claims data for downstream population-specific tasks

no code implementations24 Jun 2021 Xianlong Zeng, Simon Lin, Chang Liu

In addition, our framework showed a great generalizability potential to transfer learned knowledge from one institution to another, paving the way for future healthcare model pre-training across institutions.

Fine-tuning Transfer Learning

Sampling with Mirrored Stein Operators

1 code implementation23 Jun 2021 Jiaxin Shi, Chang Liu, Lester Mackey

We introduce a new family of particle evolution samplers suitable for constrained domains and non-Euclidean geometries.

Transformer-based unsupervised patient representation learning based on medical claims for risk stratification and analysis

no code implementations23 Jun 2021 Xianlong Zeng, Simon Lin, Chang Liu

The claims data, containing medical codes, services information, and incurred expenditure, can be a good resource for estimating an individual's health condition and medical risk level.

Representation Learning

Beyond Bounding-Box: Convex-Hull Feature Adaptation for Oriented and Densely Packed Object Detection

no code implementations CVPR 2021 Zonghao Guo, Chang Liu, Xiaosong Zhang, Jianbin Jiao, Xiangyang Ji, Qixiang Ye

Detecting oriented and densely packed objects remains challenging for spatial feature aliasing caused by the intersection of reception fields between objects.

Object Detection

Light Pollution Reduction in Nighttime Photography

no code implementations18 Jun 2021 Chang Liu, Xiaolin Wu

Nighttime photographers are often troubled by light pollution of unwanted artificial lights.

PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Driven Adaptive Prior

no code implementations11 Jun 2021 Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sungroh Yoon, Tie-Yan Liu

Denoising diffusion probabilistic models have been recently proposed to generate high-quality samples by estimating the gradient of the data density.


Hierarchical Temperature Imaging Using Pseudo-Inversed Convolutional Neural Network Aided TDLAS Tomography

no code implementations5 Jun 2021 Jingjing Si, Guoliang Li, Yinbo Cheng, Rui Zhang, Godwin Enemali, Chang Liu

As an in situ combustion diagnostic tool, Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography has been widely used for imaging of two-dimensional temperature distributions in reactive flows.

Image Reconstruction

Learning to Route via Theory-Guided Residual Network

no code implementations18 May 2021 Chang Liu, Guanjie Zheng, Zhenhui Li

Therefore, in this paper, we propose to learn the human routing model, which is one of the most essential part in the traffic simulator.

Underwater Target Recognition based on Multi-Decision LOFAR Spectrum Enhancement: A Deep Learning Approach

no code implementations26 Apr 2021 Jie Chen, Jie Liu, Chang Liu, Jian Zhang, Bing Han

To overcome this issue and to further improve the recognition performance, we adopt a deep learning approach for underwater target recognition and propose a LOFAR spectrum enhancement (LSE)-based underwater target recognition scheme, which consists of preprocessing, offline training, and online testing.

ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation

1 code implementation ICCV 2021 Kai Li, Chang Liu, Handong Zhao, Yulun Zhang, Yun Fu

This paper studies Semi-Supervised Domain Adaptation (SSDA), a practical yet under-investigated research topic that aims to learn a model of good performance using unlabeled samples and a few labeled samples in the target domain, with the help of labeled samples from a source domain.

Data Augmentation Domain Adaptation

Learnable Expansion-and-Compression Network for Few-shot Class-Incremental Learning

no code implementations6 Apr 2021 Boyu Yang, Mingbao Lin, Binghao Liu, Mengying Fu, Chang Liu, Rongrong Ji, Qixiang Ye

By tentatively expanding network nodes, LEC-Net enlarges the representation capacity of features, alleviating feature drift of old network from the perspective of model regularization.

class-incremental learning Incremental Learning

AET-EFN: A Versatile Design for Static and Dynamic Event-Based Vision

no code implementations22 Mar 2021 Chang Liu, Xiaojuan Qi, Edmund Lam, Ngai Wong

The neuromorphic event cameras, which capture the optical changes of a scene, have drawn increasing attention due to their high speed and low power consumption.

Event-based vision

Learning to Simulate on Sparse Trajectory Data

no code implementations22 Mar 2021 Hua Wei, Chacha Chen, Chang Liu, Guanjie Zheng, Zhenhui Li

Simulation of the real-world traffic can be used to help validate the transportation policies.

Imitation Learning

Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots

no code implementations17 Mar 2021 Juntao Li, Chang Liu, Chongyang Tao, Zhangming Chan, Dongyan Zhao, Min Zhang, Rui Yan

To fill the gap between these up-to-date methods and the real-world applications, we incorporate user-specific dialogue history into the response selection and propose a personalized hybrid matching network (PHMN).

Representation Learning

Ternary Hashing

no code implementations16 Mar 2021 Chang Liu, Lixin Fan, Kam Woh Ng, Yilun Jin, Ce Ju, Tianyu Zhang, Chee Seng Chan, Qiang Yang

This paper proposes a novel ternary hash encoding for learning to hash methods, which provides a principled more efficient coding scheme with performances better than those of the state-of-the-art binary hashing counterparts.

Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection

1 code implementation CVPR 2021 Bohao Li, Boyu Yang, Chang Liu, Feng Liu, Rongrong Ji, Qixiang Ye

Few-shot object detection has made substantial progressby representing novel class objects using the feature representation learned upon a set of base class objects.

Classification Few-Shot Object Detection

LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management

no code implementations6 Mar 2021 Jeremy Beauchamp, Razvan Bunescu, Cindy Marling, Zhongen Li, Chang Liu

In this work, we invert the "what-if" scenario and introduce a similar architecture based on chaining two LSTMs that can be trained to make either insulin or carbohydrate recommendations aimed at reaching a desired BG level in the future.

Time Series Time Series Forecasting

Learning Invariant Representations across Domains and Tasks

no code implementations3 Mar 2021 Jindong Wang, Wenjie Feng, Chang Liu, Chaohui Yu, Mingxuan Du, Renjun Xu, Tao Qin, Tie-Yan Liu

Being expensive and time-consuming to collect massive COVID-19 image samples to train deep classification models, transfer learning is a promising approach by transferring knowledge from the abundant typical pneumonia datasets for COVID-19 image classification.

Domain Adaptation Image Classification +1

Generalizing to Unseen Domains: A Survey on Domain Generalization

no code implementations2 Mar 2021 Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Wenjun Zeng, Tao Qin

Domain generalization deals with a challenging setting where one or several different but related domain(s) are given, and the goal is to learn a model that can generalize to an unseen test domain.

Domain Generalization Representation Learning

Target-Dependent Chemical Species Tomography with Hybrid Meshing of Sensing Regions

no code implementations10 Feb 2021 Rui Zhang, Jingjing Si, Godwin Enemali, Yong Bao, Chang Liu

The proposed scheme was both numerically and experimentally validated using a CST sensor with 32 laser beams using a variety of computational tomographic algorithms.

Towards Enhancing Fine-grained Details for Image Matting

no code implementations22 Jan 2021 Chang Liu, Henghui Ding, Xudong Jiang

In this paper, we argue that recovering these microscopic details relies on low-level but high-definition texture features.

Image Matting

Bridge the Vision Gap from Field to Command: A Deep Learning Network Enhancing Illumination and Details

no code implementations20 Jan 2021 Zhuqing Jiang, Chang Liu, Ya'nan Wang, Kai Li, Aidong Men, Haiying Wang, Haiyong Luo

With the goal of tuning up the brightness, low-light image enhancement enjoys numerous applications, such as surveillance, remote sensing and computational photography.

Low-Light Image Enhancement

Non-equilibrium Flux Rope Formation by Confined Flares Preceding a Solar Coronal Mass Ejection

no code implementations6 Jan 2021 Bernhard Kliem, Jeongwoo Lee, Rui Liu, Stephen M. White, Chang Liu, Satoshi Masuda

We present evidence that a magnetic flux rope was formed before a coronal mass ejection (CME) and its associated long-duration flare during a pair of preceding confined eruptions and associated impulsive flares in a compound event in NOAA Active Region 12371.

Solar and Stellar Astrophysics

Shed Various Lights on a Low-Light Image: Multi-Level Enhancement Guided by Arbitrary References

no code implementations4 Jan 2021 Ya'nan Wang, Zhuqing Jiang, Chang Liu, Kai Li, Aidong Men, Haiying Wang

This paper proposes a neural network for multi-level low-light image enhancement, which is user-friendly to meet various requirements by selecting different images as brightness reference.

Low-Light Image Enhancement Style Transfer

Neighbor Class Consistency on Unsupervised Domain Adaptation

no code implementations1 Jan 2021 Chang Liu, Kai Li, Yun Fu

Unsupervised domain adaptation (UDA) is to make predictions for unlabeled data in a target domain with labeled data from source domain available.

Image Classification Unsupervised Domain Adaptation

Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching

no code implementations16 Dec 2020 Chang Liu, Runzhong Wang, Zetian Jiang, Junchi Yan, Lingxiao Huang, Pinyan Lu

We propose a deep reinforcement learning (RL) based approach RGM for weighted graph matching, whose sequential node matching scheme naturally fits with the strategy for selective inlier matching against outliers, and supports seed graph matching.

Combinatorial Optimization Decision Making +1

Robustness Investigation on Deep Learning CT Reconstruction for Real-Time Dose Optimization

no code implementations7 Dec 2020 Chang Liu, Yixing Huang, Joscha Maier, Laura Klein, Marc Kachelrieß, Andreas Maier

For organ-specific AEC, a preliminary CT reconstruction is necessary to estimate organ shapes for dose optimization, where only a few projections are allowed for real-time reconstruction.

Computed Tomography (CT) Image Reconstruction

Deep Residual Network Empowered Channel Estimation for IRS-Assisted Multi-User Communication Systems

1 code implementation1 Dec 2020 Chang Liu, Xuemeng Liu, Derrick Wing Kwan Ng, Jinhong Yuan

Channel estimation is of great importance in realizing practical intelligent reflecting surface-assisted multi-user communication (IRS-MC) systems.


BLCU-NLP at SemEval-2020 Task 5: Data Augmentation for Efficient Counterfactual Detecting

no code implementations SEMEVAL 2020 Chang Liu, Dong Yu

We demonstrate the effectiveness of our approaches, which achieves 0. 95 of subtask 1 in F1 while using only a subset of giving training set to fine-tune the BERT model, and our official submission achieves F1 0. 802, which ranks us 16th in the competition.

Common Sense Reasoning Data Augmentation

Reconstruction Condition of Quantized Signals in Unlimited Sampling Framework

no code implementations29 Nov 2020 Yan He, Jifang Qiu, Chang Liu, Yue Liu, Jian Wu

The latest theoretical advances in the field of unlimited sampling framework (USF) show the potential to avoid clipping problems of analog-to-digital converters (ADC).


Cost-Effective Quasi-Parallel Sensing Instrumentation for Industrial Chemical Species Tomography

no code implementations20 Nov 2020 Godwin Enemali, Rui Zhang, Hugh McCann, Chang Liu

Although a fully parallel data acquisition (DAQ) and signal processing system can achieve these functionalities with maximised temporal response, it leads to a highly complex, expensive and power-consuming instrumentation system with high potential for inconsistency between the sampled beams due to the electronics alone.

Image Reconstruction

Towards Spatio-Temporal Video Scene Text Detection via Temporal Clustering

no code implementations19 Nov 2020 Yuanqiang Cai, Chang Liu, Weiqiang Wang, Qixiang Ye

With only bounding-box annotations in the spatial domain, existing video scene text detection (VSTD) benchmarks lack temporal relation of text instances among video frames, which hinders the development of video text-related applications.

Scene Text Scene Text Detection

Computational Design and Fabrication of Corrugated Mechanisms from Behavioral Specifications

no code implementations10 Nov 2020 Chang Liu, Wenzhong Yan, Ankur Mehta

Based on an equivalent plate model, we develop and validate analytical formulas for the behavioral specifications of OADLC mechanisms; the analytical formulas can be described as expressions of design parameters.


Deep Transfer Learning-Assisted Signal Detection for Ambient Backscatter Communications

no code implementations10 Nov 2020 Chang Liu, Xuemeng Liu, Zhiqiang Wei, Derrick Wing Kwan Ng, Jinhong Yuan, Ying-Chang Liang

Existing tag signal detection algorithms inevitably suffer from a high bit error rate (BER) due to the difficulties in estimating the channel state information (CSI).

Transfer Learning

Latent Causal Invariant Model

no code implementations4 Nov 2020 Xinwei Sun, Botong Wu, Xiangyu Zheng, Chang Liu, Wei Chen, Tao Qin, Tie-Yan Liu

To avoid spurious correlation, we propose a Latent Causal Invariance Model (LaCIM) which pursues causal prediction.

Learning Causal Semantic Representation for Out-of-Distribution Prediction

1 code implementation NeurIPS 2021 Chang Liu, Xinwei Sun, Jindong Wang, Haoyue Tang, Tao Li, Tao Qin, Wei Chen, Tie-Yan Liu

Conventional supervised learning methods, especially deep ones, are found to be sensitive to out-of-distribution (OOD) examples, largely because the learned representation mixes the semantic factor with the variation factor due to their domain-specific correlation, while only the semantic factor causes the output.

Domain Adaptation

CSTNet: A Dual-Branch Convolutional Network for Imaging of Reactive Flows using Chemical Species Tomography

no code implementations8 Oct 2020 Yunfan Jiang, Jingjing Si, Rui Zhang, Godwin Enemali, Bin Zhou, Hugh McCann, Chang Liu

Chemical Species Tomography (CST) has been widely used for in situ imaging of critical parameters, e. g. species concentration and temperature, in reactive flows.

Image Reconstruction

Location-aware Predictive Beamforming for UAV Communications: A Deep Learning Approach

no code implementations16 Sep 2020 Chang Liu, Weijie Yuan, Zhiqiang Wei, Xuemeng Liu, Derrick Wing Kwan Ng

Unmanned aerial vehicle (UAV)-assisted communication becomes a promising technique to realize the beyond fifth generation (5G) wireless networks, due to the high mobility and maneuverability of UAVs which can adapt to heterogeneous requirements of different applications.

Attribute-conditioned Layout GAN for Automatic Graphic Design

no code implementations11 Sep 2020 Jianan Li, Jimei Yang, Jianming Zhang, Chang Liu, Christina Wang, Tingfa Xu

In this paper, we introduce Attribute-conditioned Layout GAN to incorporate the attributes of design elements for graphic layout generation by forcing both the generator and the discriminator to meet attribute conditions.

Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications

no code implementations11 Sep 2020 Chang Liu, Zhiqiang Wei, Derrick Wing Kwan Ng, Jinhong Yuan, Ying-Chang Liang

To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of channel and directly recover tag symbols.

Transfer Learning

DeepSun: Machine-Learning-as-a-Service for Solar Flare Prediction

no code implementations4 Sep 2020 Yasser Abduallah, Jason T. L. Wang, Yang Nie, Chang Liu, Haimin Wang

Solar flare prediction plays an important role in understanding and forecasting space weather.

Spatio-Temporal Hierarchical Adaptive Dispatching for Ridesharing Systems

no code implementations4 Sep 2020 Chang Liu, Jiahui Sun, Haiming Jin, Meng Ai, Qun Li, Cheng Zhang, Kehua Sheng, Guobin Wu, XiaoHu Qie, Xinbing Wang

Thus, in this paper, we exploit adaptive dispatching intervals to boost the platform's profit under a guarantee of the maximum passenger waiting time.

Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications

1 code implementation3 Sep 2020 Chang Liu, Xuemeng Liu, Derrick Wing Kwan Ng, Jinhong Yuan

To this end, we first develop a versatile DReL-based channel estimation framework where a deep residual network (DRN)-based MMSE estimator is derived in terms of Bayesian philosophy.


Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning

3 code implementations27 Aug 2020 Haodi Jiang, Jiasheng Wang, Chang Liu, Ju Jing, Hao liu, Jason T. L. Wang, Haimin Wang

Deep learning has drawn a lot of interest in recent years due to its effectiveness in processing big and complex observational data gathered from diverse instruments.

Semantic Segmentation

SCNet: A Neural Network for Automated Side-Channel Attack

1 code implementation2 Aug 2020 Guanlin Li, Chang Liu, Han Yu, Yanhong Fan, Libang Zhang, Zongyue Wang, Meiqin Wang

Information about system characteristics such as power consumption, electromagnetic leaks and sound can be exploited by the side-channel attack to compromise the system.

Learning to Match Distributions for Domain Adaptation

1 code implementation17 Jul 2020 Chaohui Yu, Jindong Wang, Chang Liu, Tao Qin, Renjun Xu, Wenjie Feng, Yiqiang Chen, Tie-Yan Liu

However, it remains challenging to determine which method is suitable for a given application since they are built with certain priors or bias.

Domain Adaptation

Discretization-Aware Architecture Search

1 code implementation7 Jul 2020 Yunjie Tian, Chang Liu, Lingxi Xie, Jianbin Jiao, Qixiang Ye

The search cost of neural architecture search (NAS) has been largely reduced by weight-sharing methods.

Image Classification Neural Architecture Search

Progressive Cluster Purification for Unsupervised Feature Learning

no code implementations6 Jul 2020 Yifei Zhang, Chang Liu, Yu Zhou, Wei Wang, Weiping Wang, Qixiang Ye

In this work, we propose a novel clustering based method, which, by iteratively excluding class inconsistent samples during progressive cluster formation, alleviates the impact of noise samples in a simple-yet-effective manner.

Modeling Lost Information in Lossy Image Compression

no code implementations22 Jun 2020 Yaolong Wang, Mingqing Xiao, Chang Liu, Shuxin Zheng, Tie-Yan Liu

Specifically, ILC introduces an invertible encoding module to replace the encoder-decoder structure to produce the low dimensional informative latent representation, meanwhile, transform the lost information into an auxiliary latent variable that won't be further coded or stored.

Image Compression

Video Playback Rate Perception for Self-supervisedSpatio-Temporal Representation Learning

1 code implementation20 Jun 2020 Yuan Yao, Chang Liu, Dezhao Luo, Yu Zhou, Qixiang Ye

The generative perception model acts as a feature decoder to focus on comprehending high temporal resolution and short-term representation by introducing a motion-attention mechanism.

Action Recognition Representation Learning +1

Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks

no code implementations20 Jun 2020 Lixin Fan, Kam Woh Ng, Ce Ju, Tianyu Zhang, Chang Liu, Chee Seng Chan, Qiang Yang

This paper investigates capabilities of Privacy-Preserving Deep Learning (PPDL) mechanisms against various forms of privacy attacks.

Privacy Preserving Deep Learning

LRNNet: A Light-Weighted Network with Efficient Reduced Non-Local Operation for Real-Time Semantic Segmentation

no code implementations4 Jun 2020 Weihao Jiang, Zhaozhi Xie, Yaoyi Li, Chang Liu, Hongtao Lu

Many of these applications need to perform a real-time and efficient prediction for semantic segmentation with a light-weighted network.

Real-Time Semantic Segmentation

Towards Fine-grained Human Pose Transfer with Detail Replenishing Network

no code implementations26 May 2020 Lingbo Yang, Pan Wang, Chang Liu, Zhanning Gao, Peiran Ren, Xinfeng Zhang, Shanshe Wang, Siwei Ma, Xian-Sheng Hua, Wen Gao

Human pose transfer (HPT) is an emerging research topic with huge potential in fashion design, media production, online advertising and virtual reality.

Pose Transfer Virtual Reality

HyperSTAR: Task-Aware Hyperparameters for Deep Networks

no code implementations CVPR 2020 Gaurav Mittal, Chang Liu, Nikolaos Karianakis, Victor Fragoso, Mei Chen, Yun Fu

To reduce HPO time, we present HyperSTAR (System for Task Aware Hyperparameter Recommendation), a task-aware method to warm-start HPO for deep neural networks.

Hyperparameter Optimization Image Classification

Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce

1 code implementation17 May 2020 Juntao Li, Chang Liu, Jian Wang, Lidong Bing, Hongsong Li, Xiaozhong Liu, Dongyan Zhao, Rui Yan

We manually collect a new and high-quality paired dataset, where each pair contains an unordered product attribute set in the source language and an informative product description in the target language.

Information Retrieval

Invertible Image Rescaling

2 code implementations ECCV 2020 Mingqing Xiao, Shuxin Zheng, Chang Liu, Yaolong Wang, Di He, Guolin Ke, Jiang Bian, Zhouchen Lin, Tie-Yan Liu

High-resolution digital images are usually downscaled to fit various display screens or save the cost of storage and bandwidth, meanwhile the post-upscaling is adpoted to recover the original resolutions or the details in the zoom-in images.

Image Super-Resolution

HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment

1 code implementation11 May 2020 Lingbo Yang, Chang Liu, Pan Wang, Shanshe Wang, Peiran Ren, Siwei Ma, Wen Gao

Existing face restoration researches typically relies on either the degradation prior or explicit guidance labels for training, which often results in limited generalization ability over real-world images with heterogeneous degradations and rich background contents.

Blind Face Restoration Face Hallucination +3

Inferring Vector Magnetic Fields from Stokes Profiles of GST/NIRIS Using a Convolutional Neural Network

no code implementations8 May 2020 Hao Liu, Yan Xu, Jiasheng Wang, Ju Jing, Chang Liu, Jason T. L. Wang, Haimin Wang

By learning the latent patterns in the training data prepared by the physics-based ME tool, the proposed CNN method is able to infer vector magnetic fields from the Stokes profiles of GST/NIRIS.

Solar and Stellar Astrophysics

Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder

1 code implementation CVPR 2020 Guanlin Li, Shuya Ding, Jun Luo, Chang Liu

Whereas adversarial training is employed as the main defence strategy against specific adversarial samples, it has limited generalization capability and incurs excessive time complexity.

Adversarial Robustness Denoising +2

Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog

1 code implementation10 Mar 2020 Shen Gao, Xiuying Chen, Chang Liu, Li Liu, Dongyan Zhao, Rui Yan

Stickers with vivid and engaging expressions are becoming increasingly popular in online messaging apps, and some works are dedicated to automatically select sticker response by matching text labels of stickers with previous utterances.

Mixed Reinforcement Learning with Additive Stochastic Uncertainty

no code implementations28 Feb 2020 Yao Mu, Shengbo Eben Li, Chang Liu, Qi Sun, Bingbing Nie, Bo Cheng, Baiyu Peng

This paper presents a mixed reinforcement learning (mixed RL) algorithm by simultaneously using dual representations of environmental dynamics to search the optimal policy with the purpose of improving both learning accuracy and training speed.

Predicting Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and Recurrent Neural Networks

2 code implementations22 Feb 2020 Hao Liu, Chang Liu, Jason T. L. Wang, Haimin Wang

We present two recurrent neural networks (RNNs), one based on gated recurrent units and the other based on long short-term memory, for predicting whether an active region (AR) that produces an M- or X-class flare will also produce a coronal mass ejection (CME).

Time Series

DWM: A Decomposable Winograd Method for Convolution Acceleration

no code implementations3 Feb 2020 Di Huang, Xishan Zhang, Rui Zhang, Tian Zhi, Deyuan He, Jiaming Guo, Chang Liu, Qi Guo, Zidong Du, Shaoli Liu, Tianshi Chen, Yunji Chen

In this paper, we propose a novel Decomposable Winograd Method (DWM), which breaks through the limitation of original Winograd's minimal filtering algorithm to a wide and general convolutions.

Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning

1 code implementation2 Jan 2020 Dezhao Luo, Chang Liu, Yu Zhou, Dongbao Yang, Can Ma, Qixiang Ye, Weiping Wang

As a proxy task, it converts rich self-supervised representations into video clip operations (options), which enhances the flexibility and reduces the complexity of representation learning.

Representation Learning Self-Supervised Action Recognition +3

RGB-D Individual Segmentation

no code implementations16 Oct 2019 Wenqiang Xu, Yanjun Fu, Yuchen Luo, Chang Liu, Cewu Lu

Fine-grained recognition task deals with sub-category classification problem, which is important for real-world applications.

Straight-Through Estimator as Projected Wasserstein Gradient Flow

no code implementations5 Oct 2019 Pengyu Cheng, Chang Liu, Chunyuan Li, Dinghan Shen, Ricardo Henao, Lawrence Carin

The Straight-Through (ST) estimator is a widely used technique for back-propagating gradients through discrete random variables.

ALCNN: Attention-based Model for Fine-grained Demand Inference of Dock-less Shared Bike in New Cities

no code implementations25 Sep 2019 Chang Liu, Yanan Xu, Yanmin Zhu

In this paper, we study the problem of inferring fine-grained bike demands anywhere in a new city before the deployment of bikes.

PolSAR Image Classification Based on Dilated Convolution and Pixel-Refining Parallel Mapping network in the Complex Domain

1 code implementation24 Sep 2019 Dongling Xiao, Chang Liu, Qi. Wang, Chao Wang, Xin Zhang

For general supervised deep learning classification algorithms, the pixel-by-pixel algorithm achieves precise yet inefficient classification with a small number of labeled pixels, whereas the pixel mapping algorithm achieves efficient yet edge-rough classification with more prior labels required.

Classification General Classification +1

Distributed representation of patients and its use for medical cost prediction

no code implementations13 Sep 2019 Xianlong Zeng, Soheil Moosavinasab, En-Ju D Lin, Simon Lin, Razvan Bunescu, Chang Liu

Efficient representation of patients is very important in the healthcare domain and can help with many tasks such as medical risk prediction.

Representation Learning

FreeAnchor: Learning to Match Anchors for Visual Object Detection

2 code implementations NeurIPS 2019 Xiaosong Zhang, Fang Wan, Chang Liu, Rongrong Ji, Qixiang Ye

In this study, we propose a learning-to-match approach to break IoU restriction, allowing objects to match anchors in a flexible manner.

Object Detection

Automated Fashion Size Normalization

no code implementations27 Aug 2019 Eddie S. J. Du, Chang Liu, David H. Wayne

The ability to accurately predict the fit of fashion items and recommend the correct size is key to reducing merchandise returns in e-commerce.

Orthogonal Decomposition Network for Pixel-Wise Binary Classification

no code implementations CVPR 2019 Chang Liu, Fang Wan, Wei Ke, Zhuowei Xiao, Yuan Yao, Xiaosong Zhang, Qixiang Ye

The weight sharing scheme and spatial pooling operations in Convolutional Neural Networks (CNNs) introduce semantic correlation to neighboring pixels on feature maps and therefore deteriorate their pixel-wise classification performance.

Classification Edge Detection +3

Predicting Solar Flares Using a Long Short-Term Memory Network

2 code implementations17 May 2019 Hao Liu, Chang Liu, Jason T. L. Wang, Haimin Wang

The essence of our approach is to model data samples in an AR as time series and use LSTMs to capture temporal information of the data samples.

Time Series

CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

1 code implementation13 May 2019 Huichu Zhang, Siyuan Feng, Chang Liu, Yaoyao Ding, Yichen Zhu, Zihan Zhou, Wei-Nan Zhang, Yong Yu, Haiming Jin, Zhenhui Li

The most commonly used open-source traffic simulator SUMO is, however, not scalable to large road network and large traffic flow, which hinders the study of reinforcement learning on traffic scenarios.

Multi-agent Reinforcement Learning

Execution-Guided Neural Program Synthesis

no code implementations ICLR 2019 Xinyun Chen, Chang Liu, Dawn Song

Most existing neural program synthesis approaches employ an encoder-decoder architecture, which uses an encoder to compute the embedding of the given input-output examples, as well as a decoder to generate the program from the embedding following a given syntax.

Program Synthesis

C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection

1 code implementation CVPR 2019 Fang Wan, Chang Liu, Wei Ke, Xiangyang Ji, Jianbin Jiao, Qixiang Ye

Weakly supervised object detection (WSOD) is a challenging task when provided with image category supervision but required to simultaneously learn object locations and object detectors.

Multiple Instance Learning Weakly Supervised Object Detection +1

Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints

no code implementations10 Mar 2019 Xing Hu, Ling Liang, Lei Deng, Shuangchen Li, Xinfeng Xie, Yu Ji, Yufei Ding, Chang Liu, Timothy Sherwood, Yuan Xie

As neural networks continue their reach into nearly every aspect of software operations, the details of those networks become an increasingly sensitive subject.

Cryptography and Security Hardware Architecture

Understanding MCMC Dynamics as Flows on the Wasserstein Space

1 code implementation1 Feb 2019 Chang Liu, Jingwei Zhuo, Jun Zhu

It is known that the Langevin dynamics used in MCMC is the gradient flow of the KL divergence on the Wasserstein space, which helps convergence analysis and inspires recent particle-based variational inference methods (ParVIs).

Variational Inference

A Multiscale Image Denoising Algorithm Based On Dilated Residual Convolution Network

no code implementations21 Dec 2018 Chang Liu, Zhaowei Shang, Anyong Qin

To address this issue, here we propose a novel deep residual learning model that combines the dilated residual convolution and multi-scale convolution groups.

Image Denoising

E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs

no code implementations12 Dec 2018 Zhe Li, Caiwen Ding, Siyue Wang, Wujie Wen, Youwei Zhuo, Chang Liu, Qinru Qiu, Wenyao Xu, Xue Lin, Xuehai Qian, Yanzhi Wang

It is a challenging task to have real-time, efficient, and accurate hardware RNN implementations because of the high sensitivity to imprecision accumulation and the requirement of special activation function implementations.

automatic-speech-recognition Quantization +2

Boosting Model Performance through Differentially Private Model Aggregation

no code implementations12 Nov 2018 Sophia Collet, Robert Dadashi, Zahi N. Karam, Chang Liu, Parinaz Sobhani, Yevgeniy Vahlis, Ji Chao Zhang

In this work, two approaches for private model aggregation are proposed that enable the transfer of knowledge from existing models trained on other companies' datasets to a new company with limited labeled data while protecting each client company's underlying individual sensitive information.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

Linear Span Network for Object Skeleton Detection

no code implementations ECCV 2018 Chang Liu, Wei Ke, Fei Qin, Qixiang Ye

Hinted by this, we formalize a Linear Span framework, and propose Linear Span Network (LSN) modified by Linear Span Units (LSUs), which minimize the reconstruction error of convolutional network.

Object Skeleton Detection

Understanding and Accelerating Particle-Based Variational Inference

1 code implementation4 Jul 2018 Chang Liu, Jingwei Zhuo, Pengyu Cheng, Ruiyi Zhang, Jun Zhu, Lawrence Carin

Particle-based variational inference methods (ParVIs) have gained attention in the Bayesian inference literature, for their capacity to yield flexible and accurate approximations.

Bayesian Inference Variational Inference

Multi-task Learning for Macromolecule Classification, Segmentation and Coarse Structural Recovery in Cryo-Tomography

no code implementations16 May 2018 Chang Liu, Xiangrui Zeng, Kaiwen Wang, Qiang Guo, Min Xu

Cellular Electron Cryo-Tomography (CECT) is a powerful 3D imaging tool for studying the native structure and organization of macromolecules inside single cells.

Classification General Classification +2

Curriculum Adversarial Training

2 code implementations13 May 2018 Qi-Zhi Cai, Min Du, Chang Liu, Dawn Song

The existence of adversarial examples hinders such applications.

Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning

1 code implementation1 Apr 2018 Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, Bo Li

As machine learning becomes widely used for automated decisions, attackers have strong incentives to manipulate the results and models generated by machine learning algorithms.

Learning-Based Dequantization For Image Restoration Against Extremely Poor Illumination

no code implementations5 Mar 2018 Chang Liu, Xiaolin Wu, Xiao Shu

All existing image enhancement methods, such as HDR tone mapping, cannot recover A/D quantization losses due to insufficient or excessive lighting, (underflow and overflow problems).

Image Enhancement Image Restoration +2

The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks

no code implementations22 Feb 2018 Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, Dawn Song

This paper describes a testing methodology for quantitatively assessing the risk that rare or unique training-data sequences are unintentionally memorized by generative sequence models---a common type of machine-learning model.

Attentive Tensor Product Learning

no code implementations20 Feb 2018 Qiuyuan Huang, Li Deng, Dapeng Wu, Chang Liu, Xiaodong He

This paper proposes a new architecture - Attentive Tensor Product Learning (ATPL) - to represent grammatical structures in deep learning models.

Constituency Parsing Image Captioning +2

Generating Plans that Predict Themselves

no code implementations14 Feb 2018 Jaime F. Fisac, Chang Liu, Jessica B. Hamrick, S. Shankar Sastry, J. Karl Hedrick, Thomas L. Griffiths, Anca D. Dragan

We introduce $t$-\ACty{}: a measure that quantifies the accuracy and confidence with which human observers can predict the remaining robot plan from the overall task goal and the observed initial $t$ actions in the plan.

Deep learning based supervised semantic segmentation of Electron Cryo-Subtomograms

no code implementations12 Feb 2018 Chang Liu, Xiangrui Zeng, Ruogu Lin, Xiaodan Liang, Zachary Freyberg, Eric Xing, Min Xu

Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for the 3D visualization of cellular structure and organization at submolecular resolution.

Semantic Segmentation

Tree-to-tree Neural Networks for Program Translation

no code implementations ICLR 2018 Xinyun Chen, Chang Liu, Dawn Song

We observe that program translation is a modular procedure, in which a sub-tree of the source tree is translated into the corresponding target sub-tree at each step.


Demonstration of Topological Data Analysis on a Quantum Processor

no code implementations19 Jan 2018 He-Liang Huang, Xi-Lin Wang, Peter P. Rohde, Yi-Han Luo, You-Wei Zhao, Chang Liu, Li Li, Nai-Le Liu, Chao-Yang Lu, Jian-Wei Pan

Topological data analysis offers a robust way to extract useful information from noisy, unstructured data by identifying its underlying structure.

Topological Data Analysis

Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

no code implementations15 Dec 2017 Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, Dawn Song

In this work, we consider a new type of attacks, called backdoor attacks, where the attacker's goal is to create a backdoor into a learning-based authentication system, so that he can easily circumvent the system by leveraging the backdoor.

Data Poisoning Face Recognition

Riemannian Stein Variational Gradient Descent for Bayesian Inference

1 code implementation30 Nov 2017 Chang Liu, Jun Zhu

The benefits are two-folds: (i) for inference tasks in Euclidean spaces, RSVGD has the advantage over SVGD of utilizing information geometry, and (ii) for inference tasks on Riemann manifolds, RSVGD brings the unique advantages of SVGD to the Riemannian world.

Bayesian Inference

SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning

14 code implementations ICLR 2018 Xiaojun Xu, Chang Liu, Dawn Song

Existing state-of-the-art approaches rely on reinforcement learning to reward the decoder when it generates any of the equivalent serializations.


Message Passing Stein Variational Gradient Descent

no code implementations ICML 2018 Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, Bo Zhang

Stein variational gradient descent (SVGD) is a recently proposed particle-based Bayesian inference method, which has attracted a lot of interest due to its remarkable approximation ability and particle efficiency compared to traditional variational inference and Markov Chain Monte Carlo methods.

Bayesian Inference Variational Inference

Fooling Vision and Language Models Despite Localization and Attention Mechanism

no code implementations CVPR 2018 Xiaojun Xu, Xinyun Chen, Chang Liu, Anna Rohrbach, Trevor Darrell, Dawn Song

Our work sheds new light on understanding adversarial attacks on vision systems which have a language component and shows that attention, bounding box localization, and compositional internal structures are vulnerable to adversarial attacks.

Language understanding Natural Language Understanding +2

Anomaly Detection in Hierarchical Data Streams under Unknown Models

no code implementations11 Sep 2017 Sattar Vakili, Qing Zhao, Chang Liu, Chen-Nee Chuah

We consider the problem of detecting a few targets among a large number of hierarchical data streams.

Active Learning Anomaly Detection +1

Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection

1 code implementation22 Aug 2017 Xiaojun Xu, Chang Liu, Qian Feng, Heng Yin, Le Song, Dawn Song

The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not.

Graph Embedding Graph Matching +1

Pragmatic-Pedagogic Value Alignment

no code implementations20 Jul 2017 Jaime F. Fisac, Monica A. Gates, Jessica B. Hamrick, Chang Liu, Dylan Hadfield-Menell, Malayandi Palaniappan, Dhruv Malik, S. Shankar Sastry, Thomas L. Griffiths, Anca D. Dragan

In robotics, value alignment is key to the design of collaborative robots that can integrate into human workflows, successfully inferring and adapting to their users' objectives as they go.

Decision Making

How Much Data is Enough? A Statistical Approach with Case Study on Longitudinal Driving Behavior

no code implementations23 Jun 2017 Wenshuo Wang, Chang Liu, Ding Zhao

For projects that cost millions of dollars, it is critical to determine the right amount of data needed.

Density Estimation

Towards Synthesizing Complex Programs from Input-Output Examples

no code implementations ICLR 2018 Xinyun Chen, Chang Liu, Dawn Song

In our evaluation, we show that using our novel approach, neural parsing programs can be learned to achieve 100% test accuracy on test inputs that are 500x longer than the training samples.

Program Synthesis

Feature Analysis and Selection for Training an End-to-End Autonomous Vehicle Controller Using the Deep Learning Approach

no code implementations28 Mar 2017 Shun Yang, Wenshuo Wang, Chang Liu, Kevin Deng, J. Karl Hedrick

We collect a large set of data using The Open Racing Car Simulator (TORCS) and classify the image features into three categories (sky-related, roadside-related, and road-related features). We then design two experimental frameworks to investigate the importance of each single feature for training a CNN controller. The first framework uses the training data with all three features included to train a controller, which is then tested with data that has one feature removed to evaluate the feature's effects.

Autonomous Vehicles Feature Selection

Regularizing Face Verification Nets For Pain Intensity Regression

2 code implementations22 Feb 2017 Feng Wang, Xiang Xiang, Chang Liu, Trac. D. Tran, Austin Reiter, Gregory D. Hager, Harry Quon, Jian Cheng, Alan L. Yuille

In this way, the expression intensity regression task can benefit from the rich feature representations trained on a huge amount of data for face verification.

Face Verification Fine-tuning +1

MAT: A Multimodal Attentive Translator for Image Captioning

no code implementations18 Feb 2017 Chang Liu, Fuchun Sun, Changhu Wang, Feng Wang, Alan Yuille

In this way, the sequential representation of an image can be naturally translated to a sequence of words, as the target sequence of the RNN model.

Image Captioning Machine Translation +1

Stochastic Gradient Geodesic MCMC Methods

no code implementations NeurIPS 2016 Chang Liu, Jun Zhu, Yang song

We propose two stochastic gradient MCMC methods for sampling from Bayesian posterior distributions defined on Riemann manifolds with a known geodesic flow, e. g. hyperspheres.

Topic Models

Delving into Transferable Adversarial Examples and Black-box Attacks

1 code implementation8 Nov 2016 Yanpei Liu, Xinyun Chen, Chang Liu, Dawn Song

In this work, we are the first to conduct an extensive study of the transferability over large models and a large scale dataset, and we are also the first to study the transferability of targeted adversarial examples with their target labels.

Adversarial Attack Adversarial Defense +1