Search Results for author: Yue Liu

Found 48 papers, 11 papers with code

Personalized Entity Resolution with Dynamic Heterogeneous KnowledgeGraph Representations

no code implementations ACL (ECNLP) 2021 Ying Lin, Han Wang, Jiangning Chen, Tong Wang, Yue Liu, Heng Ji, Yang Liu, Premkumar Natarajan

We first build a cross-source heterogeneous knowledge graph from customer purchase history and product knowledge graph to jointly learn customer and product embeddings.

Entity Resolution

Simple Contrastive Graph Clustering

no code implementations11 May 2022 Yue Liu, Xihong Yang, Sihang Zhou, Xinwang Liu

To solve this problem, we propose a Simple Contrastive Graph Clustering (SCGC) algorithm to improve the existing methods from the perspectives of network architecture, data augmentation, and objective function.

Contrastive Learning Data Augmentation +3

Improved Dual Correlation Reduction Network

no code implementations25 Feb 2022 Yue Liu, Sihang Zhou, Xinwang Liu, Wenxuan Tu, Xihong Yang

Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different clusters without human annotations, is a fundamental yet challenging task.

Graph Clustering

Deep Graph Clustering via Dual Correlation Reduction

2 code implementations29 Dec 2021 Yue Liu, Wenxuan Tu, Sihang Zhou, Xinwang Liu, Linxuan Song, Xihong Yang, En Zhu

To address this issue, we propose a novel self-supervised deep graph clustering method termed Dual Correlation Reduction Network (DCRN) by reducing information correlation in a dual manner.

Graph Clustering

Temporal epistasis inference from more than 3,500,000 SARS-CoV-2 Genomic Sequences

no code implementations24 Dec 2021 Hong-Li Zeng, Yue Liu, Vito Dichio, Erik Aurell

We use Direct Coupling Analysis to determine epistatic interactions between loci of variability of the SARS-CoV-2 virus, segmenting genomes by month of sampling.

Siamese Attribute-missing Graph Auto-encoder

no code implementations9 Dec 2021 Wenxuan Tu, Sihang Zhou, Yue Liu, Xinwang Liu

First, we entangle the attribute embedding and structure embedding by introducing a siamese network structure to share the parameters learned by both processes, which allows the network training to benefit from more abundant and diverse information.

Graph Representation Learning

CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer

2 code implementations2 Dec 2021 Moein Sorkhei, Yue Liu, Hossein Azizpour, Edward Azavedo, Karin Dembrower, Dimitra Ntoula, Athanasios Zouzos, Fredrik Strand, Kevin Smith

Interval and large invasive breast cancers, which are associated with worse prognosis than other cancers, are usually detected at a late stage due to false negative assessments of screening mammograms.

Control of diffusion-driven pattern formation behind a wave of competency

1 code implementation15 Oct 2021 Yue Liu, Philip K. Maini, Ruth E. Baker

In certain biological contexts, such as the plumage patterns of birds and stripes on certain species of fishes, pattern formation takes place behind a so-called "wave of competency".

AutoNLU: Detecting, root-causing, and fixing NLU model errors

no code implementations12 Oct 2021 Pooja Sethi, Denis Savenkov, Forough Arabshahi, Jack Goetz, Micaela Tolliver, Nicolas Scheffer, Ilknur Kabul, Yue Liu, Ahmed Aly

Improving the quality of Natural Language Understanding (NLU) models, and more specifically, task-oriented semantic parsing models, in production is a cumbersome task.

Active Learning Natural Language Understanding +1

Lagrangian Inference for Ranking Problems

no code implementations1 Oct 2021 Yue Liu, Ethan X. Fang, Junwei Lu

Our proposed method aims to infer general ranking properties of the BTL model.

Mutation frequency time series reveal complex mixtures of clones in the world-wide SARS-CoV-2 viral population

no code implementations7 Sep 2021 Hong-Li Zeng, Yue Liu, Vito Dichio, Kaisa Thorell, Rickard Nordén, Erik Aurell

We compute the allele frequencies of the alpha (B. 1. 1. 7), beta (B. 1. 351) and delta (B. 167. 2) variants of SARS-CoV-2 from almost two million genome sequences on the GISAID repository.

Time Series

Blockchain-based Trustworthy Federated Learning Architecture

no code implementations16 Aug 2021 Sin Kit Lo, Yue Liu, Qinghua Lu, Chen Wang, Xiwei Xu, Hye-Young Paik, Liming Zhu

To enhance the accountability and fairness of federated learning systems, we present a blockchain-based trustworthy federated learning architecture.

Fairness Federated Learning

Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems

no code implementations NAACL 2021 Tong Wang, Jiangning Chen, Mohsen Malmir, Shuyan Dong, Xin He, Han Wang, Chengwei Su, Yue Liu, Yang Liu

In dialog systems, the Natural Language Understanding (NLU) component typically makes the interpretation decision (including domain, intent and slots) for an utterance before the mentioned entities are resolved.

Entity Resolution Intent Classification +1

Towards Demystifying Serverless Machine Learning Training

1 code implementation17 May 2021 Jiawei Jiang, Shaoduo Gan, Yue Liu, Fanlin Wang, Gustavo Alonso, Ana Klimovic, Ankit Singla, Wentao Wu, Ce Zhang

The appeal of serverless (FaaS) has triggered a growing interest on how to use it in data-intensive applications such as ETL, query processing, or machine learning (ML).

Option to survive or surrender: carbon asset management and optimization in thermal power enterprises from China

no code implementations10 Apr 2021 Yue Liu, Lixin Tian, Zhuyun Xie, Zaili Zhen, Huaping Sun

Considering the impact of price fluctuations of carbon emission right allowance, we investigate the operation of Chinese thermal power plant by modeling the decision-making with optimal stopping problem, which is established on the stochastic environment with carbon emission allowance price process simulated by geometric Brownian motion.

Decision Making

Personalized Entity Resolution with Dynamic Heterogeneous Knowledge Graph Representations

no code implementations6 Apr 2021 Ying Lin, Han Wang, Jiangning Chen, Tong Wang, Yue Liu, Heng Ji, Yang Liu, Premkumar Natarajan

For example, with "add milk to my cart", a customer may refer to a certain organic product, while some customers may want to re-order products they regularly purchase.

Entity Resolution

Beyond Visual Attractiveness: Physically Plausible Single Image HDR Reconstruction for Spherical Panoramas

no code implementations24 Mar 2021 Wei Wei, Li Guan, Yue Liu, Hao Kang, Haoxiang Li, Ying Wu, Gang Hua

By the proposed physical regularization, our method can generate HDRs which are not only visually appealing but also physically plausible.

HDR Reconstruction

Deep Learning for Android Malware Defenses: a Systematic Literature Review

1 code implementation9 Mar 2021 Yue Liu, Chakkrit Tantithamthavorn, Li Li, Yepang Liu

In this paper, we conducted a systematic literature review to search and analyze how deep learning approaches have been applied in the context of malware defenses in the Android environment.

Android Malware Detection Malware Detection +2

A Local Method for Identifying Causal Relations under Markov Equivalence

no code implementations25 Feb 2021 Zhuangyan Fang, Yue Liu, Zhi Geng, Shengyu Zhu, Yangbo He

We propose a local approach to identify whether a variable is a cause of a given target under the framework of causal graphical models of directed acyclic graphs (DAGs).

Electron heating mode transitions in radio-frequency driven micro atmospheric pressure plasma jets in He/O$_{2}$: A fluid dynamics approach

no code implementations7 Feb 2021 Yue Liu, Ihor Korolov, Torben Hemke, Lena Bischoff, Gerrit Hübner, Julian Schulze, Thomas Mussenbrock

A two-dimensional fluid model is used to investigate the electron heating dynamics and the production of neutral species in a capacitively coupled radio-frequency micro atmospheric pressure helium plasma jet -- specifically the COST jet -- with a small oxygen admixture.

Plasma Physics

On Low Rank Directed Acyclic Graphs and Causal Structure Learning

no code implementations1 Jan 2021 Zhuangyan Fang, Shengyu Zhu, Jiji Zhang, Yue Liu, Zhitang Chen, Yangbo He

Despite several important advances in recent years, learning causal structures represented by directed acyclic graphs (DAGs) remains a challenging task in high dimensional settings when the graphs to be learned are not sparse.

Electron pairing in the pseudogap state revealed by shot noise in copper-oxide junctions

no code implementations4 Dec 2020 Panpan Zhou, Liyang Chen, Yue Liu, Ilya Sochnikov, Anthony T. Bollinger, Myung-Geun Han, Yimei Zhu, Xi He, Ivan Bozovic, Douglas Natelson

In the quest to understand high-temperature superconductivity in copper oxides, a vigorous debate has been focused on the pseudogap - a partial gap that opens over portions of the Fermi surface in the 'normal' state above the bulk critical temperature ($T_{c}$).

Superconductivity Mesoscale and Nanoscale Physics Strongly Correlated Electrons

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).


Deep Neural Network Approach for Annual Luminance Simulations

no code implementations14 Sep 2020 Yue Liu, Alex Colburn, Mehlika Inanici

The proposed DNN model can faithfully predict high-quality annual panoramic luminance maps from one of the three options within 30 minutes training time: a) point-in-time luminance imagery spanning 5% of the year, when evenly distributed during daylight hours, b) one-month hourly imagery generated or collected continuously during daylight hours around the equinoxes (8% of the year); or c) 9 days of hourly data collected around the spring equinox, summer and winter solstices (2. 5% of the year) all suffice to predict the luminance maps for the rest of the year.

Blockchain-based Federated Learning for Failure Detection in Industrial IoT

no code implementations6 Sep 2020 Weishan Zhang, Qinghua Lu, Qiuyu Yu, Zhaotong Li, Yue Liu, Sin Kit Lo, Shiping Chen, Xiwei Xu, Liming Zhu

Therefore, in this paper, we present a platform architecture of blockchain-based federated learning systems for failure detection in IIoT.

Federated Learning

Video Moment Retrieval via Natural Language Queries

no code implementations4 Sep 2020 Xinli Yu, Mohsen Malmir, Cynthia He, Yue Liu, Rex Wu

However, the inference time will not be a problem for our model since our model has a simple architecture which enables efficient training and inference.

Frame Moment Retrieval

Adding Seemingly Uninformative Labels Helps in Low Data Regimes

2 code implementations ICML 2020 Christos Matsoukas, Albert Bou I Hernandez, Yue Liu, Karin Dembrower, Gisele Miranda, Emir Konuk, Johan Fredin Haslum, Athanasios Zouzos, Peter Lindholm, Fredrik Strand, Kevin Smith

Evidence suggests that networks trained on large datasets generalize well not solely because of the numerous training examples, but also class diversity which encourages learning of enriched features.

Tumor Segmentation

Decoupling Inherent Risk and Early Cancer Signs in Image-based Breast Cancer Risk Models

1 code implementation11 Jul 2020 Yue Liu, Hossein Azizpour, Fredrik Strand, Kevin Smith

With this in mind, we trained networks using three different criteria to select the positive training data (i. e. images from patients that will develop cancer): an inherent risk model trained on images with no visible signs of cancer, a cancer signs model trained on images containing cancer or early signs of cancer, and a conflated model trained on all images from patients with a cancer diagnosis.

Decision Making

Online NEAT for Credit Evaluation -- a Dynamic Problem with Sequential Data

no code implementations6 Jul 2020 Yue Liu, Adam Ghandar, Georgios Theodoropoulos

In this paper, we describe application of Neuroevolution to a P2P lending problem in which a credit evaluation model is updated based on streaming data.

online learning

Risk Variance Penalization

no code implementations13 Jun 2020 Chuanlong Xie, Haotian Ye, Fei Chen, Yue Liu, Rui Sun, Zhenguo Li

The key of the out-of-distribution (OOD) generalization is to generalize invariance from training domains to target domains.

Low Rank Directed Acyclic Graphs and Causal Structure Learning

no code implementations10 Jun 2020 Zhuangyan Fang, Shengyu Zhu, Jiji Zhang, Yue Liu, Zhitang Chen, Yangbo He

Despite several important advances in recent years, learning causal structures represented by directed acyclic graphs (DAGs) remains a challenging task in high dimensional settings when the graphs to be learned are not sparse.

Stable Prediction via Leveraging Seed Variable

no code implementations9 Jun 2020 Kun Kuang, Bo Li, Peng Cui, Yue Liu, Jianrong Tao, Yueting Zhuang, Fei Wu

By assuming the relationships between causal variables and response variable are invariant across data, to address this problem, we propose a conditional independence test based algorithm to separate those causal variables with a seed variable as priori, and adopt them for stable prediction.

Real-time Human Activity Recognition Using Conditionally Parametrized Convolutions on Mobile and Wearable Devices

no code implementations5 Jun 2020 Xin Cheng, Lei Zhang, Yin Tang, Yue Liu, Hao Wu, Jun He

For deep learning, improvements in performance have to heavily rely on increasing model size or capacity to scale to larger and larger datasets, which inevitably leads to the increase of operations.

Activity Recognition

RecipeGPT: Generative Pre-training Based Cooking Recipe Generation and Evaluation System

1 code implementation5 Mar 2020 Helena H. Lee, Ke Shu, Palakorn Achananuparp, Philips Kokoh Prasetyo, Yue Liu, Ee-Peng Lim, Lav R. Varshney

Interests in the automatic generation of cooking recipes have been growing steadily over the past few years thanks to a large amount of online cooking recipes.

Language Modelling Recipe Generation +1

Spots, strips, and spiral waves in models for static and motile cells

no code implementations23 Sep 2019 Yue Liu, Elisabeth G. Rens, Leah Edelstein-Keshet

The polarization and motility of eukaryotic cells depends on assembly and contraction of the actin cytoskeleton and its regulation by proteins called GTPases.

Estimating Glycemic Impact of Cooking Recipes via Online Crowdsourcing and Machine Learning

1 code implementation17 Sep 2019 Helena Lee, Palakorn Achananuparp, Yue Liu, Ee-Peng Lim, Lav R. Varshney

Consumption of diets with low glycemic impact is highly recommended for diabetics and pre-diabetics as it helps maintain their blood glucose levels.

Causal Discovery by Kernel Intrinsic Invariance Measure

no code implementations2 Sep 2019 Zhitang Chen, Shengyu Zhu, Yue Liu, Tim Tse

We show our algorithm can be reduced to an eigen-decomposition task on a kernel matrix measuring intrinsic deviance/invariance.

Causal Discovery

Cell size, mechanical tension, and GTPase signaling in the Single Cell

no code implementations28 Aug 2019 Andreas Buttenschön, Yue Liu, Leah Edelstein-Keshet

We further consider the feedback between mechanical tension, GTPase activation, and cell deformation in both static, growing, shrinking, and moving cells.

Exploring Stereovision-Based 3-D Scene Reconstruction for Augmented Reality

no code implementations17 Feb 2019 Guang-Yu Nie, Yun Liu, Cong Wang, Yue Liu, Yongtian Wang

Three-dimensional (3-D) scene reconstruction is one of the key techniques in Augmented Reality (AR), which is related to the integration of image processing and display systems of complex information.

Stereo Matching Stereo Matching Hand

Seq2RDF: An end-to-end application for deriving Triples from Natural Language Text

3 code implementations4 Jul 2018 Yue Liu, Tongtao Zhang, Zhicheng Liang, Heng Ji, Deborah L. McGuinness

Inspired by recent successes in neural machine translation, we treat the triples within a given knowledge graph as an independent graph language and propose an encoder-decoder framework with an attention mechanism that leverages knowledge graph embeddings.

Knowledge Graph Embeddings Translation

Recurrent knowledge distillation

no code implementations18 May 2018 Silvia L. Pintea, Yue Liu, Jan C. van Gemert

Knowledge distillation compacts deep networks by letting a small student network learn from a large teacher network.

Knowledge Distillation

Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion

no code implementations WS 2015 Yue Liu, Tao Ge, Kusum S. Mathews, Heng Ji, Deborah L. McGuinness

In the medical domain, identifying and expanding abbreviations in clinical texts is a vital task for both better human and machine understanding.

Word Embeddings

Deformable 3D Fusion: From Partial Dynamic 3D Observations to Complete 4D Models

no code implementations ICCV 2015 Weipeng Xu, Mathieu Salzmann, Yongtian Wang, Yue Liu

Capturing the 3D motion of dynamic, non-rigid objects has attracted significant attention in computer vision.

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