Search Results for author: Yue Liu

Found 89 papers, 29 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

A Reliable Representation with Bidirectional Transition Model for Visual Reinforcement Learning Generalization

no code implementations4 Dec 2023 Xiaobo Hu, Youfang Lin, Yue Liu, Jinwen Wang, Shuo Wang, Hehe Fan, Kai Lv

Visual reinforcement learning has proven effective in solving control tasks with high-dimensional observations.

Navigating Privacy and Copyright Challenges Across the Data Lifecycle of Generative AI

no code implementations30 Nov 2023 Dawen Zhang, Boming Xia, Yue Liu, Xiwei Xu, Thong Hoang, Zhenchang Xing, Mark Staples, Qinghua Lu, Liming Zhu

The advent of Generative AI has marked a significant milestone in artificial intelligence, demonstrating remarkable capabilities in generating realistic images, texts, and data patterns.

Data Poisoning

SparseSpikformer: A Co-Design Framework for Token and Weight Pruning in Spiking Transformer

no code implementations15 Nov 2023 Yue Liu, Shanlin Xiao, Bo Li, Zhiyi Yu

As the third-generation neural network, the Spiking Neural Network (SNN) has the advantages of low power consumption and high energy efficiency, making it suitable for implementation on edge devices.

Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey

1 code implementation27 Oct 2023 Xinyu She, Yue Liu, Yanjie Zhao, Yiling He, Li Li, Chakkrit Tantithamthavorn, Zhan Qin, Haoyu Wang

After carefully examining these studies, we designed a taxonomy of pitfalls in LM4Code research and conducted a systematic study to summarize the issues, implications, current solutions, and challenges of different pitfalls for LM4Code systems.

Code Generation

Constructing Synthetic Treatment Groups without the Mean Exchangeability Assumption

no code implementations28 Sep 2023 Yuhang Zhang, Yue Liu, Zhihua Zhang

Motivated by the synthetic control method, we construct a synthetic treatment group for the target population by a weighted mixture of treatment groups of source populations.

At Which Training Stage Does Code Data Help LLMs Reasoning?

1 code implementation28 Sep 2023 Yingwei Ma, Yue Liu, Yue Yu, Yuanliang Zhang, Yu Jiang, Changjian Wang, Shanshan Li

Inspired by the great success of code data in training LLMs, we naturally wonder at which training stage introducing code data can really help LLMs reasoning.

Question Answering

AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model

no code implementations27 Sep 2023 Seungwhan Moon, Andrea Madotto, Zhaojiang Lin, Tushar Nagarajan, Matt Smith, Shashank Jain, Chun-Fu Yeh, Prakash Murugesan, Peyman Heidari, Yue Liu, Kavya Srinet, Babak Damavandi, Anuj Kumar

We present Any-Modality Augmented Language Model (AnyMAL), a unified model that reasons over diverse input modality signals (i. e. text, image, video, audio, IMU motion sensor), and generates textual responses.

Language Modelling Video Question Answering

TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classification

1 code implementation21 Sep 2023 Meng Liu, Ke Liang, Dayu Hu, Hao Yu, Yue Liu, Lingyuan Meng, Wenxuan Tu, Sihang Zhou, Xinwang Liu

We observe that these audiovisual data naturally have temporal attributes, such as the time information for each frame in the video.

Graph Learning

Parameter identifiability and model selection for partial differential equation models of cell invasion

1 code implementation4 Sep 2023 Yue Liu, Kevin Suh, Philip K. Maini, Daniel J. Cohen, Ruth E. Baker

When employing mechanistic models to study biological phenomena, practical parameter identifiability is important for making accurate predictions across wide range of unseen scenarios, as well as for understanding the underlying mechanisms.

Experimental Design Model Selection

Large Language Models for Software Engineering: A Systematic Literature Review

no code implementations21 Aug 2023 Xinyi Hou, Yanjie Zhao, Yue Liu, Zhou Yang, Kailong Wang, Li Li, Xiapu Luo, David Lo, John Grundy, Haoyu Wang

Nevertheless, a comprehensive understanding of the application, effects, and possible limitations of LLMs on SE is still in its early stages.

Head-to-Tail: How Knowledgeable are Large Language Models (LLM)? A.K.A. Will LLMs Replace Knowledge Graphs?

no code implementations20 Aug 2023 Kai Sun, Yifan Ethan Xu, Hanwen Zha, Yue Liu, Xin Luna Dong

Since the recent prosperity of Large Language Models (LLMs), there have been interleaved discussions regarding how to reduce hallucinations from LLM responses, how to increase the factuality of LLMs, and whether Knowledge Graphs (KGs), which store the world knowledge in a symbolic form, will be replaced with LLMs.

Knowledge Graphs World Knowledge

DealMVC: Dual Contrastive Calibration for Multi-view Clustering

1 code implementation17 Aug 2023 Xihong Yang, Jiaqi Jin, Siwei Wang, Ke Liang, Yue Liu, Yi Wen, Suyuan Liu, Sihang Zhou, Xinwang Liu, En Zhu

Then, a global contrastive calibration loss is proposed by aligning the view feature similarity graph and the high-confidence pseudo-label graph.

Clustering Pseudo Label

CONVERT:Contrastive Graph Clustering with Reliable Augmentation

1 code implementation17 Aug 2023 Xihong Yang, Cheng Tan, Yue Liu, Ke Liang, Siwei Wang, Sihang Zhou, Jun Xia, Stan Z. Li, Xinwang Liu, En Zhu

To address these problems, we propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (CONVERT).

Clustering Contrastive Learning +4

Reinforcement Graph Clustering with Unknown Cluster Number

2 code implementations13 Aug 2023 Yue Liu, Ke Liang, Jun Xia, Xihong Yang, Sihang Zhou, Meng Liu, Xinwang Liu, Stan Z. Li

To enable the deep graph clustering algorithms to work without the guidance of the predefined cluster number, we propose a new deep graph clustering method termed Reinforcement Graph Clustering (RGC).

Clustering Graph Clustering +1

Decentralised Governance for Foundation Model based AI Systems: Exploring the Role of Blockchain in Responsible AI

no code implementations11 Aug 2023 Yue Liu, Qinghua Lu, Liming Zhu, Hye-Young Paik

Foundation models including large language models (LLMs) are increasingly attracting interest worldwide for their distinguished capabilities and potential to perform a wide variety of tasks.

Structure Guided Multi-modal Pre-trained Transformer for Knowledge Graph Reasoning

no code implementations6 Jul 2023 Ke Liang, Sihang Zhou, Yue Liu, Lingyuan Meng, Meng Liu, Xinwang Liu

To this end, we propose the graph Structure Guided Multimodal Pretrained Transformer for knowledge graph reasoning, termed SGMPT.

Knowledge Graphs Question Answering +2

Analytical reconstructions of full-scan multiple source-translation computed tomography under large field of views

no code implementations31 May 2023 Zhisheng Wang, Yue Liu, Shunli Wang, Xingyuan Bian, Zongfeng Li, Junning Cui

This paper is to investigate the high-quality analytical reconstructions of multiple source-translation computed tomography (mSTCT) under an extended field of view (FOV).

Dink-Net: Neural Clustering on Large Graphs

2 code implementations28 May 2023 Yue Liu, Ke Liang, Jun Xia, Sihang Zhou, Xihong Yang, Xinwang Liu, Stan Z. Li

Subsequently, the clustering distribution is optimized by minimizing the proposed cluster dilation loss and cluster shrink loss in an adversarial manner.

Clustering Graph Clustering +1

Distributed Trust Through the Lens of Software Architecture

no code implementations25 May 2023 Sin Kit Lo, Yue Liu, Guangsheng Yu, Qinghua Lu, Xiwei Xu, Liming Zhu

Distributed trust is a nebulous concept that has evolved from different perspectives in recent years.

Federated Learning

Message Intercommunication for Inductive Relation Reasoning

no code implementations23 May 2023 Ke Liang, Lingyuan Meng, Sihang Zhou, Siwei Wang, Wenxuan Tu, Yue Liu, Meng Liu, Xinwang Liu

However, the uni-directional message-passing mechanism hinders such models from exploiting hidden mutual relations between entities in directed graphs.

Knowledge Graphs

Deep Temporal Graph Clustering

no code implementations18 May 2023 Meng Liu, Yue Liu, Ke Liang, Siwei Wang, Sihang Zhou, Xinwang Liu

To solve the problem, we propose a general framework for deep Temporal Graph Clustering called TGC, which adjusts deep clustering techniques (clustering assignment distribution and adjacency matrix reconstruction) to suit the interaction sequence-based batch-processing pattern of temporal graphs.

Clustering Deep Clustering +3

SARF: Aliasing Relation Assisted Self-Supervised Learning for Few-shot Relation Reasoning

no code implementations20 Apr 2023 Lingyuan Meng, Ke Liang, Bin Xiao, Sihang Zhou, Yue Liu, Meng Liu, Xihong Yang, Xinwang Liu

Moreover, most of the existing methods ignore leveraging the beneficial information from aliasing relations (AR), i. e., data-rich relations with similar contextual semantics to the target data-poor relation.

Knowledge Graphs Self-Supervised Learning

Poisson Conjugate Prior for PHD Filtering based Track-Before-Detect Strategies in Radar Systems

no code implementations22 Feb 2023 Haiyi Mao, Cong Peng, Yue Liu, Jinping Tang, Hua Peng, Wei Yi

A variety of filters with track-before-detect (TBD) strategies have been developed and applied to low signal-to-noise ratio (SNR) scenarios, including the probability hypothesis density (PHD) filter.

Industrial computed tomography based intelligent non-destructive testing method for power capacitor

no code implementations6 Feb 2023 Zhenxing Cheng, Peng Wang, Yue Liu, Wei Qin, Zidi Tang

Power capacitor device is a widely used reactive power compensation equipment in power transmission and distribution system which can easily have internal fault and therefore affects the safe operation of the power system.

Data Augmentation

Cluster-guided Contrastive Graph Clustering Network

1 code implementation3 Jan 2023 Xihong Yang, Yue Liu, Sihang Zhou, Siwei Wang, Wenxuan Tu, Qun Zheng, Xinwang Liu, Liming Fang, En Zhu

Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views.

Clustering Contrastive Learning +1

Decentralized Gradient Tracking with Local Steps

no code implementations3 Jan 2023 Yue Liu, Tao Lin, Anastasia Koloskova, Sebastian U. Stich

Gradient tracking (GT) is an algorithm designed for solving decentralized optimization problems over a network (such as training a machine learning model).

Swin MAE: Masked Autoencoders for Small Datasets

1 code implementation28 Dec 2022 Zi'an Xu, Yin Dai, Fayu Liu, Weibing Chen, Yue Liu, Lifu Shi, Sheng Liu, YuHang Zhou

The development of deep learning models in medical image analysis is majorly limited by the lack of large-sized and well-annotated datasets.

Transfer Learning

Hard Sample Aware Network for Contrastive Deep Graph Clustering

2 code implementations16 Dec 2022 Yue Liu, Xihong Yang, Sihang Zhou, Xinwang Liu, Zhen Wang, Ke Liang, Wenxuan Tu, Liang Li, Jingcan Duan, Cancan Chen

Moreover, under the guidance of the carefully collected high-confidence clustering information, our proposed weight modulating function will first recognize the positive and negative samples and then dynamically up-weight the hard sample pairs while down-weighting the easy ones.

Clustering Graph Clustering

A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal

1 code implementation12 Dec 2022 Ke Liang, Lingyuan Meng, Meng Liu, Yue Liu, Wenxuan Tu, Siwei Wang, Sihang Zhou, Xinwang Liu, Fuchun Sun

According to the graph types, existing KGR models can be roughly divided into three categories, i. e., static models, temporal models, and multi-modal models.

General Knowledge Knowledge Graph Embedding +3

Attribute Graph Clustering via Learnable Augmentation

no code implementations7 Dec 2022 Xihong Yang, Yue Liu, Ke Liang, Sihang Zhou, Xinwang Liu, En Zhu

To this end, we propose an Attribute Graph Clustering method via Learnable Augmentation (\textbf{AGCLA}), which introduces learnable augmentors for high-quality and suitable augmented samples for CDGC.

Clustering Contrastive Learning +3

Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View

no code implementations1 Dec 2022 Jingcan Duan, Siwei Wang, Pei Zhang, En Zhu, Jingtao Hu, Hu Jin, Yue Liu, Zhibin Dong

However, they neglect the subgraph-subgraph comparison information which the normal and abnormal subgraph pairs behave differently in terms of embeddings and structures in GAD, resulting in sub-optimal task performance.

Contrastive Learning Graph Anomaly Detection

Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure

no code implementations19 Nov 2022 Ke Liang, Yue Liu, Sihang Zhou, Wenxuan Tu, Yi Wen, Xihong Yang, Xiangjun Dong, Xinwang Liu

To this end, we propose a knowledge graph contrastive learning framework based on relation-symmetrical structure, KGE-SymCL, which mines symmetrical structure information in KGs to enhance the discriminative ability of KGE models.

Contrastive Learning Graph Learning +4

High Dynamic Range Image Quality Assessment Based on Frequency Disparity

1 code implementation6 Sep 2022 Yue Liu, Zhangkai Ni, Shiqi Wang, Hanli Wang, Sam Kwong

In this paper, a novel and effective image quality assessment (IQA) algorithm based on frequency disparity for high dynamic range (HDR) images is proposed, termed as local-global frequency feature-based model (LGFM).

Image Quality Assessment Vocal Bursts Intensity Prediction

PatchDropout: Economizing Vision Transformers Using Patch Dropout

1 code implementation10 Aug 2022 Yue Liu, Christos Matsoukas, Fredrik Strand, Hossein Azizpour, Kevin Smith

This simple approach, PatchDropout, reduces FLOPs and memory by at least 50% in standard natural image datasets such as ImageNet, and those savings only increase with image size.

Image Classification Medical Image Classification

Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction

no code implementations25 Jul 2022 Huaying Hao, Cong Xu, Dan Zhang, Qifeng Yan, Jiong Zhang, Yue Liu, Yitian Zhao

To be more specific, we first perform a simple degradation of the 3x3 mm2/high-resolution (HR) image to obtain the synthetic LR image.

Domain Adaptation Image Super-Resolution

Multiple Kernel Clustering with Dual Noise Minimization

no code implementations13 Jul 2022 Junpu Zhang, Liang Li, Siwei Wang, Jiyuan Liu, Yue Liu, Xinwang Liu, En Zhu

As a representative, late fusion MKC first decomposes the kernels into orthogonal partition matrices, then learns a consensus one from them, achieving promising performance recently.


On the Representation of Causal Background Knowledge and its Applications in Causal Inference

no code implementations10 Jul 2022 Zhuangyan Fang, Ruiqi Zhao, Yue Liu, Yangbo He

Causal background knowledge about the existence or the absence of causal edges and paths is frequently encountered in observational studies.

Causal Inference

Spatial Transformation for Image Composition via Correspondence Learning

no code implementations6 Jul 2022 Bo Zhang, Yue Liu, Kaixin Lu, Li Niu, Liqing Zhang

Instead, we propose a novel correspondence learning network (CorrelNet) to model the correspondence between foreground and background using cross-attention maps, based on which we can predict the target coordinate that each source coordinate of foreground should be mapped to on the background.

Virtual Try-on

Mixed Graph Contrastive Network for Semi-Supervised Node Classification

no code implementations6 Jun 2022 Xihong Yang, Yue Liu, Sihang Zhou, Xinwang Liu, En Zhu

Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised node classification in recent years.

Classification Contrastive Learning +4

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.

Clustering Contrastive Learning +4

FedSynth: Gradient Compression via Synthetic Data in Federated Learning

1 code implementation4 Apr 2022 Shengyuan Hu, Jack Goetz, Kshitiz Malik, Hongyuan Zhan, Zhe Liu, Yue Liu

Model compression is important in federated learning (FL) with large models to reduce communication cost.

Federated Learning Model Compression

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.

Clustering 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.

Clustering 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 (DCA) 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 Time Series Analysis

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 +1

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 +2

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).

BIG-bench Machine Learning

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 Management

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 Single-shot 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

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 Privacy Preserving

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.

Moment Retrieval Natural Language Queries +1

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.

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.

On 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 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.

Human 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.

BIG-bench Machine Learning

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 +1

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

Cannot find the paper you are looking for? You can Submit a new open access paper.