Search Results for author:
Found 42 papers, 7 papers with code
LEGO-ABSA: A Prompt-based Task Assemblable Unified Generative Framework for Multi-task Aspect-based Sentiment Analysis
This paper proposes a unified generative multi-task framework that can solve multiple ABSA tasks by controlling the type of task prompts consisting of multiple element prompts.
Ranked #5 on
Aspect-Based Sentiment Analysis (ABSA)
on TASD
(using extra training data)
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
FSMLP: Modelling Channel Dependencies With Simplex Theory Based Multi-Layer Perceptions In Frequency Domain
In this paper, we investigate the overfitting problem in channel-wise MLPs using Rademacher complexity theory, revealing that extreme values in time series data exacerbate this issue.
Open-World Dynamic Prompt and Continual Visual Representation Learning
Our DPaRL learns to generate dynamic prompts for inference, as opposed to relying on a static prompt pool in previous PCL methods.
Line Spectral Estimation with Unlimited Sensing
To overcome the noise sensitivity of higher-order difference-based methods, we explore the properties of the first-order difference of modulo samples, and develop two line spectral estimation algorithms based on first-order difference, which are robust against noise.
CSI-Free Position Optimization for Movable Antenna Communication Systems: A Black-Box Optimization Approach
Movable antenna (MA) is a new technology which leverages local movement of antennas to improve channel qualities and enhance the communication performance.
Intelligent Reflecting Surface-Assisted NLOS Sensing With OFDM Signals
This work addresses the problem of intelligent reflecting surface (IRS) assisted target sensing in a non-line-of-sight (NLOS) scenario, where an IRS is employed to facilitate the radar/access point (AP) to sense the targets when the line-of-sight (LOS) path between the AP and the target is blocked by obstacles.
Near/Far-Field Channel Estimation For Terahertz Systems With ELAAs: A Block-Sparse-Aware Approach
In this paper, we consider the problem of hybrid near/far-field channel estimation by taking spherical wave propagation into account.
Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approach
Due to the potentially massive number of users involved, it is crucial to reduce the communication overhead of the CFL system.
Empower Your Model with Longer and Better Context Comprehension
Recently, with the emergence of numerous Large Language Models (LLMs), the implementation of AI has entered a new era.
Threshold-Consistent Margin Loss for Open-World Deep Metric Learning
Existing losses used in deep metric learning (DML) for image retrieval often lead to highly non-uniform intra-class and inter-class representation structures across test classes and data distributions.
Line Spectrum Estimation and Detection with Few-bit ADCs: Theoretical Analysis and Generalized NOMP Algorithm
As radar systems will be equipped with thousands of antenna elements and wide bandwidth, the associated costs and power consumption become exceedingly high, and a potential solution is to adopt low-resolution quantization technology, which not only reduces data storage needs but also lowers power and hardware costs.
Musketeer: Joint Training for Multi-task Vision Language Model with Task Explanation Prompts
We present a vision-language model whose parameters are jointly trained on all tasks and fully shared among multiple heterogeneous tasks which may interfere with each other, resulting in a single model which we named Musketeer.
Target-Mounted Intelligent Reflecting Surface for Joint Location and Orientation Estimation
In this paper, we propose a new application of IRS for device-free target sensing via joint location and orientation estimation.
Compressed CPD-Based Channel Estimation and Joint Beamforming for RIS-Assisted Millimeter Wave Communications
Also, by utilizing the singular value decomposition-like structure of the effective channel, this paper develops a joint active and passive beamforming method based on the estimated cascade channels.
An In-depth Study of Stochastic Backpropagation
In this paper, we provide an in-depth study of Stochastic Backpropagation (SBP) when training deep neural networks for standard image classification and object detection tasks.
Towards Regression-Free Neural Networks for Diverse Compute Platforms
With the shift towards on-device deep learning, ensuring a consistent behavior of an AI service across diverse compute platforms becomes tremendously important.
Confederated Learning: Federated Learning with Decentralized Edge Servers
Most studies on FL consider a centralized framework, in which a single server is endowed with a central authority to coordinate a number of devices to perform model training in an iterative manner.
Over-the-Air Federated Multi-Task Learning via Model Sparsification and Turbo Compressed Sensing
To achieve communication-efficient federated multitask learning (FMTL), we propose an over-the-air FMTL (OAFMTL) framework, where multiple learning tasks deployed on edge devices share a non-orthogonal fading channel under the coordination of an edge server (ES).
Spatial Channel Covariance Estimation and Two-Timescale Beamforming for IRS-Assisted Millimeter Wave Systems
We consider the problem of spatial channel covariance matrix (CCM) estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication systems.
Compressed Channel Estimation for IRS-Assisted Millimeter Wave OFDM Systems: A Low-Rank Tensor Decomposition-Based Approach
We consider the problem of downlink channel estimation for intelligent reflecting surface (IRS)-assisted millimeter Wave (mmWave) orthogonal frequency division multiplexing (OFDM) systems.
Beam Training and Alignment for RIS-Assisted Millimeter Wave Systems:State of the Art and Beyond
The deployment of RIS, however, complicates the system architecture and poses a significant challenge for beam training (BT)/ beam alignment (BA), a process that is required to establish a reliable link between the transmitter and the receiver.
Fast and Efficient Once-For-All Networks for Diverse Hardware Deployment
In this work, we continue the effort to reduce the training cost of OFA methods.
Joint Active and Passive Beamforming for IRS-Assisted Radar
Intelligent reflecting surface (IRS) is a promising technology being considered for future wireless communications due to its ability to control signal propagation.
Over-the-Air Federated Multi-Task Learning
In this letter, we introduce over-the-air computation into the communication design of federated multi-task learning (FMTL), and propose an over-the-air federated multi-task learning (OA-FMTL) framework, where multiple learning tasks deployed on edge devices share a non-orthogonal fading channel under the coordination of an edge server (ES).
Recent Advances on Sub-Nyquist Sampling-Based Wideband Spectrum Sensing
Cognitive radio (CR) is a promising technology enabling efficient utilization of the spectrum resource for future wireless systems.
Fast Beam Training and Alignment for IRS-Assisted Millimeter Wave/Terahertz Systems
Intelligent reflecting surface (IRS) has emerged as a competitive solution to address blockage issues in millimeter wave (mmWave) and Terahertz (THz) communications due to its capability of reshaping wireless transmission environments.
Session-based Recommendation with Self-Attention Networks
The self-attention networks (SAN) allow SR-SAN capture the global dependencies among all items of a session regardless of their distance.
Reconfigurable Intelligent Surface Aided Constant-Envelope Wireless Power Transfer
By reconfiguring the propagation environment of electromagnetic waves artificially, reconfigurable intelligent surfaces (RISs) have been regarded as a promising and revolutionary hardware technology to improve the energy and spectrum efficiency of wireless networks.
Reconfigurable Intelligent Surface Aided Constant-Envelope Wireless Power Transfer
By reconfiguring the propagation environment of electromagnetic waves artificially, reconfigurable intelligent surfaces (RISs) have been regarded as a promising and revolutionary hardware technology to improve the energy and spectrum efficiency of wireless networks.
Post-Training Piecewise Linear Quantization for Deep Neural Networks
Quantization plays an important role in the energy-efficient deployment of deep neural networks on resource-limited devices.
Compressed Channel Estimation and Joint Beamforming for Intelligent Reflecting Surface-Assisted Millimeter Wave Systems
In this paper, we consider channel estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) systems, where an IRS is deployed to assist the data transmission from the base station (BS) to a user.
Intelligent Reflecting Surface-Assisted Millimeter Wave Communications: Joint Active and Passive Precoding Design
In this framework, we study joint active and passive precoding design for IRS-assisted mmWave systems, where multiple IRSs are deployed to assist the data transmission from a base station (BS) to a single-antenna receiver.
Low-Rank Phase Retrieval via Variational Bayesian Learning
We propose a hierarchical prior model for low-rank phase retrieval, in which a Gaussian-Wishart hierarchical prior is placed on the underlying low-rank matrix to promote the low-rankness of the matrix.
Simultaneous Block-Sparse Signal Recovery Using Pattern-Coupled Sparse Bayesian Learning
In this paper, we consider the block-sparse signals recovery problem in the context of multiple measurement vectors (MMV) with common row sparsity patterns.
Bayesian Compressive Sensing Using Normal Product Priors
In this paper, we introduce a new sparsity-promoting prior, namely, the "normal product" prior, and develop an efficient algorithm for sparse signal recovery under the Bayesian framework.
Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models
The problem of low rank matrix completion is considered in this paper.
Robust Bayesian Compressed sensing
We consider the problem of robust compressed sensing whose objective is to recover a high-dimensional sparse signal from compressed measurements corrupted by outliers.
Low-Rank Tensor Decomposition-Aided Channel Estimation for Millimeter Wave MIMO-OFDM Systems
Different from most existing studies that are concerned with narrowband channels, we consider estimation of wideband mmWave channels with frequency selectivity, which is more appropriate for mmWave MIMO-OFDM systems.
Information Theory Information Theory
An Iterative Reweighted Method for Tucker Decomposition of Incomplete Multiway Tensors
In this paper, we focus on Tucker decomposition which represents an Nth-order tensor in terms of N factor matrices and a core tensor via multilinear operations.
Global and Local Structure Preserving Sparse Subspace Learning: An Iterative Approach to Unsupervised Feature Selection
In this paper, we propose a global and local structure preserving sparse subspace learning (GLoSS) model for unsupervised feature selection.
Sparse Bayesian Dictionary Learning with a Gaussian Hierarchical Model
In this paper, we propose a new hierarchical Bayesian model for dictionary learning, in which a Gaussian-inverse Gamma hierarchical prior is used to promote the sparsity of the representation.
Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals
In this paper, we develop a new sparse Bayesian learning method for recovery of block-sparse signals with unknown cluster patterns.
