Search Results for author:
Found 67 papers, 24 papers with code
Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis
Our research demonstrates a notable advancement in diabetes prediction over traditional methods, showcasing the effectiveness of our combined BiLSTM-CRF, XGBoost, and Logistic Regression model.
Optimized CNNs for Rapid 3D Point Cloud Object Recognition
This study introduces a method for efficiently detecting objects within 3D point clouds using convolutional neural networks (CNNs).
MambaVLT: Time-Evolving Multimodal State Space Model for Vision-Language Tracking
Witnessing its success, we propose a Mamba-based vision-language tracking model to exploit its state space evolving ability in temporal space for robust multimodal tracking, dubbed MambaVLT.
GTA-Net: An IoT-Integrated 3D Human Pose Estimation System for Real-Time Adolescent Sports Posture Correction
To address these issues, we propose GTA-Net, an intelligent system for posture correction and real-time feedback in adolescent sports, integrated within an IoT-enabled environment.
Real-time Monitoring and Analysis of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm
This research focuses on real-time monitoring and analysis of track and field athletes, addressing the limitations of traditional monitoring systems in terms of real-time performance and accuracy.
Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement
The rapid development of large language models (LLMs), like ChatGPT, has resulted in the widespread presence of LLM-generated content on social media platforms, raising concerns about misinformation, data biases, and privacy violations, which can undermine trust in online discourse.
ASQuery: A Query-based Model for Action Segmentation
For the task of temporal action segmentation, existing works commonly treat it as a frame-wise classification problem.
Ranked #1 on
Action Segmentation
on Assembly101
Instruction-guided Multi-Granularity Segmentation and Captioning with Large Multimodal Model
Observing the lack of a benchmark for model training and evaluation over the MGSC task, we establish a benchmark with aligned masks and captions in multi-granularity using our customized automated annotation pipeline.
A Compressive Memory-based Retrieval Approach for Event Argument Extraction
Recent works have demonstrated the effectiveness of retrieval augmentation in the Event Argument Extraction (EAE) task.
Enhancing Visual Question Answering through Ranking-Based Hybrid Training and Multimodal Fusion
This work highlights the effectiveness of a ranking-based hybrid training strategy in improving VQA performance and lays the groundwork for further research in multimodal learning methods.
ISMRNN: An Implicitly Segmented RNN Method with Mamba for Long-Term Time Series Forecasting
Long time series forecasting aims to utilize historical information to forecast future states over extended horizons.
ESCoT: Towards Interpretable Emotional Support Dialogue Systems
Understanding the reason for emotional support response is crucial for establishing connections between users and emotional support dialogue systems.
FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture
Food is a rich and varied dimension of cultural heritage, crucial to both individuals and social groups.
Physics-informed Score-based Diffusion Model for Limited-angle Reconstruction of Cardiac Computed Tomography
Cardiac computed tomography (CT) has emerged as a major imaging modality for the diagnosis and monitoring of cardiovascular diseases.
Emulating Full Client Participation: A Long-Term Client Selection Strategy for Federated Learning
In this study, we propose a novel client selection strategy designed to emulate the performance achieved with full client participation.
Harnessing Vision-Language Pretrained Models with Temporal-Aware Adaptation for Referring Video Object Segmentation
As images and texts are mapped to uncoupled feature spaces, they face the arduous task of learning Vision-Language (VL) relation modeling from scratch.
Referring Expression Segmentation
Referring Video Object Segmentation
+3
High-dimensional multiple imputation (HDMI) for partially observed confounders including natural language processing-derived auxiliary covariates
HDMI approaches may decrease bias in studies with partially observed confounders where missingness depends on unobserved factors.
Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction
Recent mainstream event argument extraction methods process each event in isolation, resulting in inefficient inference and ignoring the correlations among multiple events.
Does Mapo Tofu Contain Coffee? Probing LLMs for Food-related Cultural Knowledge
Recent studies have highlighted the presence of cultural biases in Large Language Models (LLMs), yet often lack a robust methodology to dissect these phenomena comprehensively.
ControlUDA: Controllable Diffusion-assisted Unsupervised Domain Adaptation for Cross-Weather Semantic Segmentation
Data generation is recognized as a potent strategy for unsupervised domain adaptation (UDA) pertaining semantic segmentation in adverse weathers.
MLPs Compass: What is learned when MLPs are combined with PLMs?
While Transformer-based pre-trained language models and their variants exhibit strong semantic representation capabilities, the question of comprehending the information gain derived from the additional components of PLMs remains an open question in this field.
SynSP: Synergy of Smoothness and Precision in Pose Sequences Refinement
To leverage this quality information we propose a motion refinement network termed SynSP to achieve a Synergy of Smoothness and Precision in the sequence refinement tasks.
Meta-DSP: A Meta-Learning Approach for Data-Driven Nonlinear Compensation in High-Speed Optical Fiber Systems
Non-linear effects in long-haul, high-speed optical fiber systems significantly hinder channel capacity.
Unmasking Bias in AI: A Systematic Review of Bias Detection and Mitigation Strategies in Electronic Health Record-based Models
Sixty proposed various strategies for mitigating biases, especially targeting implicit and selection biases.
CreoleVal: Multilingual Multitask Benchmarks for Creoles
Creoles represent an under-explored and marginalized group of languages, with few available resources for NLP research. While the genealogical ties between Creoles and a number of highly-resourced languages imply a significant potential for transfer learning, this potential is hampered due to this lack of annotated data.
Cultural Adaptation of Recipes
We introduce a new task involving the translation and cultural adaptation of recipes between Chinese and English-speaking cuisines.
Copyright Violations and Large Language Models
Language models may memorize more than just facts, including entire chunks of texts seen during training.
LoMAE: Low-level Vision Masked Autoencoders for Low-dose CT Denoising
Recently, transformer models emerged as a promising avenue to enhance LDCT image quality.
Rethinking Relation Classification with Graph Meaning Representations
In the field of natural language understanding, the intersection of neural models and graph meaning representations (GMRs) remains a compelling area of research.
Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features
The increasing ubiquity of language technology necessitates a shift towards considering cultural diversity in the machine learning realm, particularly for subjective tasks that rely heavily on cultural nuances, such as Offensive Language Detection (OLD).
Utilizing Contextual Clues and Role Correlations for Enhancing Document-level Event Argument Extraction
Document-level event argument extraction is a crucial yet challenging task within the field of information extraction.
Unified Single-Stage Transformer Network for Efficient RGB-T Tracking
With this structure, the network can extract fusion features of the template and search region under the mutual interaction of modalities.
Ranked #4 on
Rgb-T Tracking
on GTOT
Streamlining Social Media Information Retrieval for COVID-19 Research with Deep Learning
Conclusions: This study advances public health research by implementing a novel, systematic pipeline for curating symptom lexicons from social media data.
Counterpart Fairness -- Addressing Systematic between-group Differences in Fairness Evaluation
When using machine learning to aid decision-making, it is critical to ensure that an algorithmic decision is fair and does not discriminate against specific individuals/groups, particularly those from underprivileged populations.
Cross-Cultural Transfer Learning for Chinese Offensive Language Detection
Detecting offensive language is a challenging task.
Cultural Vocal Bursts Intensity Prediction
Few-Shot Learning
+1
Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study
The recent release of ChatGPT has garnered widespread recognition for its exceptional ability to generate human-like responses in dialogue.
Joint Visual Grounding and Tracking with Natural Language Specification
Such a separated framework overlooks the link between visual grounding and tracking, which is that the natural language descriptions provide global semantic cues for localizing the target for both two steps.
Ranked #3 on
Visual Tracking
on TNL2K
Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications
Time-trend analysis indicated that Hydroxychloroquine and Ivermectin were discussed more than Molnupiravir and Remdesivir, particularly during COVID-19 surges.
GraphEye: A Novel Solution for Detecting Vulnerable Functions Based on Graph Attention Network
GraphEye is originated from the observation that the code property graph of a non-vulnerable function naturally differs from the code property graph of a vulnerable function with the same functionality.
DPGNN: Dual-Perception Graph Neural Network for Representation Learning
To address these drawbacks, we present a novel message-passing paradigm, based on the properties of multi-step message source, node-specific message output, and multi-space message interaction.
Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning
Motivated by suggested question generation in conversational news recommendation systems, we propose a model for generating question-answer pairs (QA pairs) with self-contained, summary-centric questions and length-constrained, article-summarizing answers.
Spatial-Temporal Deep Intention Destination Networks for Online Travel Planning
Therefore, in this paper, we focus on predicting users' intention destinations in online travel platforms.
Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks
With the development of the 5G and Internet of Things, amounts of wireless devices need to share the limited spectrum resources.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Scalable Power Control/Beamforming in Heterogeneous Wireless Networks with Graph Neural Networks
Machine learning (ML) has been widely used for efficient resource allocation (RA) in wireless networks.
Deep Convolutional Neural Networks to Predict Mutual Coupling Effects in Metasurfaces
Metasurfaces have provided a novel and promising platform for the realization of compact and large-scale optical devices.
Summary-Oriented Question Generation for Informational Queries
Our model shows SOTA performance of SQ generation on the NQ dataset (20. 1 BLEU-4).
Research on AI Composition Recognition Based on Music Rules
Starting from the essence of the music, the article constructs a music-rule-identifying algorithm through extracting modes, which will identify the stability of the mode of machine-generated music, to judge whether it is artificial intelligent.
Inverse Reinforcement Learning with Natural Language Goals
In this paper, we propose a novel adversarial inverse reinforcement learning algorithm to learn a language-conditioned policy and reward function.
XiaoiceSing: A High-Quality and Integrated Singing Voice Synthesis System
This paper presents XiaoiceSing, a high-quality singing voice synthesis system which employs an integrated network for spectrum, F0 and duration modeling.
A Freeform Dielectric Metasurface Modeling Approach Based on Deep Neural Networks
Metasurfaces have shown promising potentials in shaping optical wavefronts while remaining compact compared to bulky geometric optics devices.
Proq: Projection-based Runtime Assertions for Debugging on a Quantum Computer
In this paper, we propose Proq, a runtime assertion scheme for testing and debugging quantum programs on a quantum computer.
Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answering
Multi-domain dialogue state tracking (DST) is a critical component for conversational AI systems.
Ranked #9 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Extreme Learning Machine-Based Heterogeneous Domain Adaptation for Classification of Hyperspectral Images
An extreme learning machine (ELM)-based heterogeneous domain adaptation (HDA) algorithm is proposed for the classification of remote sensing images.
Multifunctional Metasurface Design with a Generative Adversarial Network
Metasurfaces have enabled precise electromagnetic wave manipulation with strong potential to obtain unprecedented functionalities and multifunctional behavior in flat optical devices.
A Novel Modeling Approach for All-Dielectric Metasurfaces Using Deep Neural Networks
Metasurfaces have become a promising means for manipulating optical wavefronts in flat and high-performance optical devices.
Cycle-SUM: Cycle-consistent Adversarial LSTM Networks for Unsupervised Video Summarization
The evaluator defines a learnable information preserving metric between original video and summary video and "supervises" the selector to identify the most informative frames to form the summary video.
Ranked #7 on
Unsupervised Video Summarization
on TvSum
The Design and Implementation of XiaoIce, an Empathetic Social Chatbot
This paper describes the development of Microsoft XiaoIce, the most popular social chatbot in the world.
Modeling Treatment Delays for Patients using Feature Label Pairs in a Time Series
Pharmaceutical targeting is one of key inputs for making sales and marketing strategy planning.
RNNFast: An Accelerator for Recurrent Neural Networks Using Domain Wall Memory
At the same time, the sequential nature of input/weight processing of RNNs mitigates one of the downsides of DWM, which is the linear (rather than constant) data access time. RNNFast is very efficient and highly scalable, with flexible mapping of logical neurons to RNN hardware blocks.
Object Relation Detection Based on One-shot Learning
Detecting the relations among objects, such as "cat on sofa" and "person ride horse", is a crucial task in image understanding, and beneficial to bridging the semantic gap between images and natural language.
Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing
Despite the noticeable progress in perceptual tasks like detection, instance segmentation and human parsing, computers still perform unsatisfactorily on visually understanding humans in crowded scenes, such as group behavior analysis, person re-identification and autonomous driving, etc.
Ranked #1 on
Multi-Human Parsing
on PASCAL-Part
End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient
Learning a goal-oriented dialog policy is generally performed offline with supervised learning algorithms or online with reinforcement learning (RL).
Learning for Disparity Estimation through Feature Constancy
The second part performs matching cost calculation, matching cost aggregation and disparity calculation to estimate the initial disparity using shared features.
Latent Contextual Bandits and their Application to Personalized Recommendations for New Users
We consider both the benefit of leveraging a set of learned latent user classes for new users, and how we can learn such latent classes from prior users.
A Survey on Contextual Multi-armed Bandits
In this survey we cover a few stochastic and adversarial contextual bandit algorithms.
A Note on Information-Directed Sampling and Thompson Sampling
This note introduce three Bayesian style Multi-armed bandit algorithms: Information-directed sampling, Thompson Sampling and Generalized Thompson Sampling.
