Search Results for author:
Found 74 papers, 33 papers with code
Composition-based Heterogeneous Graph Multi-channel Attention Network for Multi-aspect Multi-sentiment Classification
Aspect-based sentiment analysis (ABSA) has drawn more and more attention because of its extensive applications.
Aspect-Based Sentiment Analysis (ABSA)
Sentiment Classification
Semi-supervised Intent Discovery with Contrastive Learning
User intent discovery is a key step in developing a Natural Language Understanding (NLU) module at the core of any modern Conversational AI system.
Chinese Intermediate English Learners outdid ChatGPT in deep cohesion: Evidence from English narrative writing
The data were analyzed in terms of five discourse components using Coh-Metrix (a special instrument for analyzing language discourses), and the results revealed that ChatGPT performed better than human writers in narrativity, word concreteness, and referential cohesion, but worse in syntactic simplicity and deep cohesion in its initial version.
High speed free-space optical communication using standard fiber communication component without optical amplification
Free-space optical communication (FSO) can achieve fast, secure and license-free communication without need for physical cables, making it a cost-effective, energy-efficient and flexible solution when the fiber connection is unavailable.
TIGER: Temporal Interaction Graph Embedding with Restarts
However, due to the entangled temporal and structural dependencies, existing methods have to process the sequence of events chronologically and consecutively to ensure node representations are up-to-date.
ConsRec: Learning Consensus Behind Interactions for Group Recommendation
Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation task.
What Decreases Editing Capability? Domain-Specific Hybrid Refinement for Improved GAN Inversion
To tackle this problem, we introduce Domain-Specific Hybrid Refinement (DHR), which draws on the advantages and disadvantages of two mainstream refinement techniques to maintain editing ability with fidelity improvement.
CL-CrossVQA: A Continual Learning Benchmark for Cross-Domain Visual Question Answering
Visual Question Answering (VQA) is a multi-discipline research task.
Learning to Learn Domain-invariant Parameters for Domain Generalization
Due to domain shift, deep neural networks (DNNs) usually fail to generalize well on unknown test data in practice.
SAP-DETR: Bridging the Gap Between Salient Points and Queries-Based Transformer Detector for Fast Model Convergency
To bridge the gap between the reference points of salient queries and Transformer detectors, we propose SAlient Point-based DETR (SAP-DETR) by treating object detection as a transformation from salient points to instance objects.
RuDi: Explaining Behavior Sequence Models by Automatic Statistics Generation and Rule Distillation
We propose a two-stage method, RuDi, that distills the knowledge of black-box teacher models into rule-based student models.
ReMix: A General and Efficient Framework for Multiple Instance Learning based Whole Slide Image Classification
We evaluate ReMix on two public datasets with two state-of-the-art MIL methods.
ReCo: A Dataset for Residential Community Layout Planning
In order to address the issues and advance a benchmark dataset for various intelligent spatial design and analysis applications in the development of smart city, we introduce Residential Community Layout Planning (ReCo) Dataset, which is the first and largest open-source vector dataset related to real-world community to date.
mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation
Concretely, we propose a novel multimodal Medical Transformer (mmFormer) for incomplete multimodal learning with three main components: the hybrid modality-specific encoders that bridge a convolutional encoder and an intra-modal Transformer for both local and global context modeling within each modality; an inter-modal Transformer to build and align the long-range correlations across modalities for modality-invariant features with global semantics corresponding to tumor region; a decoder that performs a progressive up-sampling and fusion with the modality-invariant features to generate robust segmentation.
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images
In this paper, we propose a Decoupled Pyramid Correlation Network (DPC-Net) that exploits attention mechanisms to fully leverage both low- and high-level features embedded in FCN to segment liver tumor.
Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy
To this end, we contribute to advance the study of the proactive dialogue policy to a more natural and challenging setting, i. e., interacting dynamically with users.
Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension
Procedural Multimodal Documents (PMDs) organize textual instructions and corresponding images step by step.
Collaborative Driving: Learning- Aided Joint Topology Formulation and Beamforming
Currently, autonomous vehicles are able to drive more naturally based on the driving policies learned from millions of driving miles in real environments.
ECOLA: Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations
Since conventional knowledge embedding models cannot take full advantage of the abundant textual information, there have been extensive research efforts in enhancing knowledge embedding using texts.
Graph Attention Transformer Network for Multi-Label Image Classification
Subsequently, we design the graph attention transformer layer to transfer this adjacency matrix to adapt to the current domain.
Triangle Graph Interest Network for Click-through Rate Prediction
Due to the elusiveness of user interests, those works still fail to determine the real motivation of user click behaviors.
Learning Based Task Offloading in Digital Twin Empowered Internet of Vehicles
In specific, to preserve the precious computing resource at different levels for most appropriate computing tasks, we integrate a learning scheme based on the prediction of futuristic computing tasks in DT.
Reinforcement Learning Enhanced Explainer for Graph Neural Networks
To address these issues, we propose a RL-enhanced GNN explainer, RG-Explainer, which consists of three main components: starting point selection, iterative graph generation and stopping criteria learning.
SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes
Most of the medical observational studies estimate the causal treatment effects using electronic health records (EHR), where a patient's covariates and outcomes are both observed longitudinally.
A Survey of Visual Transformers
Transformer, an attention-based encoder-decoder model, has already revolutionized the field of natural language processing (NLP).
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms
Missing data is an important problem in machine learning practice.
A semi-automatic ultrasound image analysis system for the grading diagnosis of COVID-19 pneumonia
This paper proposes a semi-automatic system based on quantitative characterization of the specific image patterns in lung ultrasound (LUS) images, in order to assess the lung conditions of patients with COVID-19 pneumonia, as well as to differentiate between the severe / and no-severe cases.
Fact-Tree Reasoning for N-ary Question Answering over Knowledge Graphs
In the question answering(QA) task, multi-hop reasoning framework has been extensively studied in recent years to perform more efficient and interpretable answer reasoning on the Knowledge Graph(KG).
How Powerful is Graph Convolution for Recommendation?
In this paper, we endeavor to obtain a better understanding of GCN-based CF methods via the lens of graph signal processing.
Ranked #5 on
Collaborative Filtering
on Gowalla
Adaptive Multi-Resolution Attention with Linear Complexity
Transformers have improved the state-of-the-art across numerous tasks in sequence modeling.
Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects
Conditional average treatment effects (CATEs) allow us to understand the effect heterogeneity across a large population of individuals.
Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation
To this end, we propose a novel mutual learning (ML) strategy for effective and robust multi-modal liver tumor segmentation.
TumorCP: A Simple but Effective Object-Level Data Augmentation for Tumor Segmentation
Experiments on kidney tumor segmentation task demonstrate that TumorCP surpasses the strong baseline by a remarkable margin of 7. 12% on tumor Dice.
Measurement of the absolute branching fractions for purely leptonic $D_s^+$ decays
Constraining our measurement to the Standard Model expectation of lepton universality ($R=9. 75$), we find the more precise results $\cal B(D_s^+\to \tau^+\nu_\tau) = (5. 22\pm0. 10\pm 0. 14)\times10^{-2}$ and $A_{\it CP}(\tau^\pm\nu_\tau) = (-0. 1\pm1. 9\pm1. 0)\%$.
High Energy Physics - Experiment
Cross section measurement of $e^+e^- \to p\bar{p}η$ and $e^+e^- \to p\bar{p}ω$ at center-of-mass energies between 3.773 GeV and 4.6 GeV
Based on $14. 7~\textrm{fb}^{-1}$ of $e^+e^-$ annihilation data collected with the BESIII detector at the BEPCII collider at 17 different center-of-mass energies between $3. 7730~\textrm{GeV}$ and $4. 5995~\textrm{GeV}$, Born cross sections of the two processes $e^+e^- \to p\bar{p}\eta$ and $e^+e^- \to p\bar{p}\omega$ are measured for the first time.
High Energy Physics - Experiment
SyncTwin: Transparent Treatment Effect Estimation under Temporal Confounding
Estimating causal treatment effects using observational data is a problem with few solutions when the confounder has a temporal structure, e. g. the history of disease progression might impact both treatment decisions and clinical outcomes.
Measurements of the center-of-mass energies of $e^{+}e^{-}$ collisions at BESIII
During the 2016-17 and 2018-19 running periods, the BESIII experiment collected 7. 5~fb$^{-1}$ of $e^+e^-$ collision data at center-of-mass energies ranging from 4. 13 to 4. 44 GeV.
High Energy Physics - Experiment
Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer
Accurate segmentation of cardiac structures can assist doctors to diagnose diseases, and to improve treatment planning, which is highly demanded in the clinical practice.
Exploring Instance-Level Uncertainty for Medical Detection
The ability of deep learning to predict with uncertainty is recognized as key for its adoption in clinical routines.
Generalized Relation Learning with Semantic Correlation Awareness for Link Prediction
The current methods for the link prediction taskhavetwonaturalproblems:1)the relation distributions in KGs are usually unbalanced, and 2) there are many unseen relations that occur in practical situations.
Argument Mining Driven Analysis of Peer-Reviews
Peer reviewing is a central process in modern research and essential for ensuring high quality and reliability of published work.
Search for the reaction $e^{+}e^{-} \rightarrow π^{+}π^{-} χ_{cJ}$ and a charmonium-like structure decaying to $χ_{cJ}π^{\pm}$ between 4.18 and 4.60 GeV
We search for the process $e^{+}e^{-}\rightarrow \pi ^{+}\pi ^{-} \chi_{cJ}$ ($J=0, 1, 2$) and for a charged charmonium-like state in the $\pi ^{\pm} \chi_{cJ}$ subsystem.
High Energy Physics - Experiment
VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain
We also introduce a novel tabular data augmentation method for self- and semi-supervised learning frameworks.
Gradient Regularized V-Learning for Dynamic Treatment Regimes
A dynamic treatment regime (DTR) is a sequence of treatment rules indicating how to individualize treatments for a patient based on the previously assigned treatments and the evolving covariate history.
Learning outside the Black-Box: The pursuit of interpretable models
Machine Learning has proved its ability to produce accurate models but the deployment of these models outside the machine learning community has been hindered by the difficulties of interpreting these models.
AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?
With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets.
Double-Uncertainty Weighted Method for Semi-supervised Learning
We train the teacher model using Bayesian deep learning to obtain double-uncertainty, i. e. segmentation uncertainty and feature uncertainty.
Modality-Pairing Learning for Brain Tumor Segmentation
In this work, we propose a novel end-to-end Modality-Pairing learning method for brain tumor segmentation.
GMH: A General Multi-hop Reasoning Model for KG Completion
To the best of our knowledge, there lacks a general framework that approaches multi-hop reasoning in mixed long-short distance reasoning scenarios.
CASTLE: Regularization via Auxiliary Causal Graph Discovery
Regularization improves generalization of supervised models to out-of-sample data.
Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning
Instead of learning on the complete input graph data, with a novel data augmentation strategy, \textsc{Subg-Con} learns node representations through a contrastive loss defined based on subgraphs sampled from the original graph instead.
Woodpecker-DL: Accelerating Deep Neural Networks via Hardware-Aware Multifaceted Optimizations
Accelerating deep model training and inference is crucial in practice.
Train Your Data Processor: Distribution-Aware and Error-Compensation Coordinate Decoding for Human Pose Estimation
Recently, the leading performance of human pose estimation is dominated by heatmap based methods.
Model independent determination of the spin of the $Ω^{-}$ and its polarization alignment in $ψ(3686)\rightarrowΩ^{-}\barΩ^{+}$
We present an analysis of the process $\psi(3686) \to \Omega^- \bar{\Omega}^+$ ($\Omega^-\to K^-\Lambda$, $\bar{\Omega}^+\to K^+\bar{\Lambda}$, $\Lambda\to p\pi^-$, $\bar{\Lambda}\to \bar{p}\pi^+$) based on a data set of $448\times 10^6$ $\psi(3686)$ decays collected with the BESIII detector at the BEPCII electron-positron collider.
High Energy Physics - Experiment
AutoCP: Automated Pipelines for Accurate Prediction Intervals
Successful application of machine learning models to real-world prediction problems, e. g. financial forecasting and personalized medicine, has proved to be challenging, because such settings require limiting and quantifying the uncertainty in the model predictions, i. e. providing valid and accurate prediction intervals.
Does Non-COVID19 Lung Lesion Help? Investigating Transferability in COVID-19 CT Image Segmentation
Coronavirus disease 2019 (COVID-19) is a highly contagious virus spreading all around the world.
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification
Most of the current methods of subgroup analysis begin with a particular algorithm for estimating individualized treatment effects (ITE) and identify subgroups by maximizing the difference across subgroups of the average treatment effect in each subgroup.
Sybil-proof Answer Querying Mechanism
We study a question answering problem on a social network, where a requester is seeking an answer from the agents on the network.
Computer Science and Game Theory
Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network
This paper strives to learn fine-grained fashion similarity.
Learning Overlapping Representations for the Estimation of Individualized Treatment Effects
The choice of making an intervention depends on its potential benefit or harm in comparison to alternatives.
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning
In this paper, we propose a novel Bayesian optimization (BO) algorithm to tackle the challenge of model selection in this setting.
The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge
The 2019 Kidney and Kidney Tumor Segmentation challenge (KiTS19) was a competition held in conjunction with the 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) which sought to address these issues and stimulate progress on this automatic segmentation problem.
Semantic Feature Attention Network for Liver Tumor Segmentation in Large-scale CT database
In the SFAN, a Semantic Attention Transmission (SAT) module is designed to select discriminative low-level localization details with the guidance of neighboring high-level semantic information.
Cascaded Volumetric Convolutional Network for Kidney Tumor Segmentation from CT volumes
Automated segmentation of kidney and tumor from 3D CT scans is necessary for the diagnosis, monitoring, and treatment planning of the disease.
Lifelong Bayesian Optimization
In this paper, we present Lifelong Bayesian Optimization (LBO), an online, multitask Bayesian optimization (BO) algorithm designed to solve the problem of model selection for datasets arriving and evolving over time.
Collaborative Data Acquisition
We consider a requester who acquires a set of data (e. g. images) that is not owned by one party.
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning
Predicting bioactivity and physical properties of small molecules is a central challenge in drug discovery.
PocketFlow: An Automated Framework for Compressing and Accelerating Deep Neural Networks
Deep neural networks are widely used in various domains, but the prohibitive computational complexity prevents their deployment on mobile devices.
Geometry of energy landscapes and the optimizability of deep neural networks
Deep neural networks are workhorse models in machine learning with multiple layers of non-linear functions composed in series.
Dermoscopic Image Analysis for ISIC Challenge 2018
This short paper reports the algorithms we used and the evaluation performances for ISIC Challenge 2018.
BigDL: A Distributed Deep Learning Framework for Big Data
This paper presents BigDL (a distributed deep learning framework for Apache Spark), which has been used by a variety of users in the industry for building deep learning applications on production big data platforms.
Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning
We derive a correspondence between parameter inference and free energy minimisation in statistical physics.
Application of Convolutional Neural Network to Predict Airfoil Lift Coefficient
The adaptability of the convolutional neural network (CNN) technique for aerodynamic meta-modeling tasks is probed in this work.
Distributed Representation of Subgraphs
Motivated by the recent successes of embeddings in natural language processing, researchers have tried to find network embeddings in order to exploit machine learning algorithms for mining tasks like node classification and edge prediction.
Ranked #4 on
Malware Detection
on Android Malware Dataset
