Search Results for author:
Found 45 papers, 15 papers with code
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.
CreoleVal: Multilingual Multitask Benchmarks for Creoles
Creoles represent an under-explored and marginalized group of languages, with few available resources for NLP research.
Unmasking Bias and Inequities: A Systematic Review of Bias Detection and Mitigation in Healthcare Artificial Intelligence Using Electronic Health Records
Conclusion: Despite growing attention to bias in healthcare AI, research using EHR data on this topic is still limited.
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.
Revisiting Graph Meaning Representations through Decoupling Contextual Representation Learning and Structural Information Propagation
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).
CARLG: Leveraging Contextual Clues and Role Correlations for Improving Document-level Event Argument Extraction
In this paper, we propose the CARLG model, which consists of two modules: Contextual Clues Aggregation (CCA) and Role-based Latent Information Guidance (RLIG), effectively leveraging contextual clues and role correlations for improving document-level EAE.
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.
Streamlining Social Media Information Retrieval for Public Health Research with Deep Learning
The utilization of social media in epidemic surveillance has been well established.
Counterpart Fairness -- Addressing Systematic between-group Differences in Fairness Evaluation
The confounding factors, which are non-sensitive variables but manifest systematic differences, can significantly affect fairness evaluation.
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 revealed that Hydroxychloroquine and Ivermectin received much more discussion 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.
A Dual-Perception Graph Neural Network with Multi-hop Graph Generator
In DPGNN, we utilize node features to construct a feature graph, and perform node representations learning based on the original topology graph and the constructed feature graph simultaneously, which conduce to capture the structural neighborhood information and the feature-related information.
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.
