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Found 52 papers, 10 papers with code
Satellite Federated Edge Learning: Architecture Design and Convergence Analysis
Although promising, the dynamics of LEO networks, characterized by the high mobility of satellites and short ground-to-satellite link (GSL) duration, pose unique challenges for FEEL.
Learning from Reduced Labels for Long-Tailed Data
Long-tailed data is prevalent in real-world classification tasks and heavily relies on supervised information, which makes the annotation process exceptionally labor-intensive and time-consuming.
Determined Multi-Label Learning via Similarity-Based Prompt
In this novel labeling setting, each training instance is associated with a \textit{determined label} (either "Yes" or "No"), which indicates whether the training instance contains the provided class label.
Dual Encoder: Exploiting the Potential of Syntactic and Semantic for Aspect Sentiment Triplet Extraction
In this work, we propose a \emph{Dual Encoder: Exploiting the potential of Syntactic and Semantic} model (D2E2S), which maximizes the syntactic and semantic relationships among words.
Extensible Multi-Granularity Fusion Network for Aspect-based Sentiment Analysis
This paper presents the Extensible Multi-Granularity Fusion (EMGF) network, which integrates information from dependency and constituent syntactic, attention semantic , and external knowledge graphs.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
A Comprehensive Dataset and Automated Pipeline for Nailfold Capillary Analysis
In this study, we present a pioneering effort in constructing a comprehensive nailfold capillary dataset-321 images, 219 videos from 68 subjects, with clinic reports and expert annotations-that serves as a crucial resource for training deep-learning models.
Efficient Decoder for End-to-End Oriented Object Detection in Remote Sensing Images
To address these limitations, we propose an end-to-end oriented detector equipped with an efficient decoder, which incorporates two technologies, Rotated RoI attention (RRoI attention) and Selective Distinct Queries (SDQ).
Over-the-Air Federated Learning and Optimization
We first provide a comprehensive study on the convergence of AirComp-based FedAvg (AirFedAvg) algorithms under both strongly convex and non-convex settings with constant and diminishing learning rates in the presence of data heterogeneity.
Towards Scalable Wireless Federated Learning: Challenges and Solutions
The explosive growth of smart devices (e. g., mobile phones, vehicles, drones) with sensing, communication, and computation capabilities gives rise to an unprecedented amount of data.
CT-Net: Arbitrary-Shaped Text Detection via Contour Transformer
Contour based scene text detection methods have rapidly developed recently, but still suffer from inaccurate frontend contour initialization, multi-stage error accumulation, or deficient local information aggregation.
LRANet: Towards Accurate and Efficient Scene Text Detection with Low-Rank Approximation Network
Next, we propose a dual assignment scheme for speed acceleration.
Integrated Sensing-Communication-Computation for Edge Artificial Intelligence
Edge artificial intelligence (AI) has been a promising solution towards 6G to empower a series of advanced techniques such as digital twins, holographic projection, semantic communications, and auto-driving, for achieving intelligence of everything.
Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization
To improve the learning performance, we establish a system optimization framework by joint transceiver and fronthaul quantization design, for which successive convex approximation and alternate convex search based system optimization algorithms are developed.
Learning from Stochastic Labels
Annotating multi-class instances is a crucial task in the field of machine learning.
Machine Learning for Large-Scale Optimization in 6G Wireless Networks
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from "connected things" to "connected intelligence", featured by ultra high density, large-scale, dynamic heterogeneity, diversified functional requirements and machine learning capabilities, which leads to a growing need for highly efficient intelligent algorithms.
Proximal Gradient-Based Unfolding for Massive Random Access in IoT Networks
Simulations show that the proposed unfolding neural network achieves better recovery performance, convergence rate, and adaptivity than current baselines.
Federated Learning via Unmanned Aerial Vehicle
To enable communication-efficient federated learning (FL), this paper studies an unmanned aerial vehicle (UAV)-enabled FL system, where the UAV coordinates distributed ground devices for a shared model training.
Over-the-Air Computation: Foundations, Technologies, and Applications
The rapid advancement of artificial intelligence technologies has given rise to diversified intelligent services, which place unprecedented demands on massive connectivity and gigantic data aggregation.
Class-Imbalanced Complementary-Label Learning via Weighted Loss
In such scenarios, the number of samples in one class is considerably lower than in other classes, which consequently leads to a decline in the accuracy of predictions.
Trustworthy Federated Learning via Blockchain
In this paper, we shall propose a decentralized blockchain based FL (B-FL) architecture by using a secure global aggregation algorithm to resist malicious devices, and deploying practical Byzantine fault tolerance consensus protocol with high effectiveness and low energy consumption among multiple edge servers to prevent model tampering from the malicious server.
TextDCT: Arbitrary-Shaped Text Detection via Discrete Cosine Transform Mask
Arbitrary-shaped scene text detection is a challenging task due to the variety of text changes in font, size, color, and orientation.
Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks
We characterize the Pareto boundary of the error-induced gap region to quantify the learning performance trade-off among different FL tasks, based on which we formulate an optimization problem to minimize the sum of error-induced gaps in all cells.
DDAC-SpAM: A Distributed Algorithm for Fitting High-dimensional Sparse Additive Models with Feature Division and Decorrelation
The effectiveness and efficiency of the proposed algorithm are demonstrated through theoretical analysis and empirical results on both synthetic and real data.
Gan-Based Joint Activity Detection and Channel Estimation For Grant-free Random Access
Joint activity detection and channel estimation (JADCE) for grant-free random access is a critical issue that needs to be addressed to support massive connectivity in IoT networks.
Differentially Private Federated Learning via Reconfigurable Intelligent Surface
Federated learning (FL), as a disruptive machine learning paradigm, enables the collaborative training of a global model over decentralized local datasets without sharing them.
Over-the-Air Federated Learning via Second-Order Optimization
To address this issue, in this paper, we instead propose a novel over-the-air second-order federated optimization algorithm to simultaneously reduce the communication rounds and enable low-latency global model aggregation.
Knowledge-Guided Learning for Transceiver Design in Over-the-Air Federated Learning
Based on the convergence analysis, we formulate an optimization problem to minimize the upper bound to enhance the learning performance, followed by proposing an alternating optimization algorithm to facilitate the optimal transceiver design for AirComp-assisted FL.
Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning
Under non-convex settings, we derive the convergence performance of the FedZO algorithm on non-independent and identically distributed data and characterize the impact of the numbers of local iterates and participating edge devices on the convergence.
Show, Deconfound and Tell: Image Captioning With Causal Inference
In the encoding stage, the IOD is able to disentangle the region-based visual features by deconfounding the visual confounder.
Pairwise Learning for Neural Link Prediction
The framework treats link prediction as a pairwise learning to rank problem and consists of four main components, i. e., neighborhood encoder, link predictor, negative sampler and objective function.
Ranked #1 on
Link Property Prediction
on ogbl-citation2
Learning Proximal Operator Methods for Massive Connectivity in IoT Networks
To mitigate all the aforementioned limitations, we in this paper develop an effective unfolding neural network framework built upon the proximal operator method to tackle the JADCE problem in IoT networks, where the base station is equipped with multiple antennas.
Sparse Signal Processing for Massive Connectivity via Mixed-Integer Programming
Although an enhanced estimation performance in terms of the mean squared error (MSE) can be achieved, the weighted $l_1$-norm minimization algorithm is still a convex relaxation of the original group-sparse matrix estimation problem, yielding a suboptimal solution.
Over-the-Air Computation via Cloud Radio Access Networks
Over-the-air computation (AirComp) has recently been recognized as a promising scheme for a fusion center to achieve fast distributed data aggregation in wireless networks via exploiting the superposition property of multiple-access channels.
Algorithm Unrolling for Massive Access via Deep Neural Network with Theoretical Guarantee
Moreover, the proposed algorithm unrolling approach inherits the structure and domain knowledge of the ISTA, thereby maintaining the algorithm robustness, which can handle non-Gaussian preamble sequence matrix in massive access.
Over-the-Air Decentralized Federated Learning
In this paper, we consider decentralized federated learning (FL) over wireless networks, where over-the-air computation (AirComp) is adopted to facilitate the local model consensus in a device-to-device (D2D) communication manner.
UAV Aided Over-the-Air Computation
Over-the-air computation (AirComp) seamlessly integrates communication and computation by exploiting the waveform superposition property of multiple-access channels.
Optimal Receive Beamforming for Over-the-Air Computation
In this paper, we consider fast wireless data aggregation via over-the-air computation (AirComp) in Internet of Things (IoT) networks, where an access point (AP) with multiple antennas aim to recover the arithmetic mean of sensory data from multiple IoT devices.
Over-the-Air Computation via Reconfigurable Intelligent Surface
Over-the-air computation (AirComp) is a disruptive technique for fast wireless data aggregation in Internet of Things (IoT) networks via exploiting the waveform superposition property of multiple-access channels.
UAV-Assisted Over-the-Air Computation
Over-the-air computation (AirComp) provides a promising way to support ultrafast aggregation of distributed data.
Optimize the trajectory of UAV which plays a BS in communication system
A new volatility model: GQARCH-Itô model
Volatility asymmetry is a hot topic in high-frequency financial market.
Methodology
Federated Learning via Intelligent Reflecting Surface
To optimize the learning performance, we formulate an optimization problem that jointly optimizes the device selection, the aggregation beamformer at the base station (BS), and the phase shifts at the IRS to maximize the number of devices participating in the model aggregation of each communication round under certain mean-squared-error (MSE) requirements.
Fast Convergence Algorithm for Analog Federated Learning
In this paper, we consider federated learning (FL) over a noisy fading multiple access channel (MAC), where an edge server aggregates the local models transmitted by multiple end devices through over-the-air computation (AirComp).
Reconfigurable Intelligent Surface Enhanced Cognitive Radio Networks
The cognitive radio (CR) network is a promising network architecture that meets the requirement of enhancing scarce radio spectrum utilization.
Reconfigurable Intelligent Surface for Interference Alignment in MIMO Device-to-Device Networks
In multiple-input multiple-output (MIMO) device-to-device (D2D) networks, interference and rank-deficient channels are the critical bottlenecks for achieving high degrees of freedom (DoFs).
Vehicle Re-Identification Based on Complementary Features
Top performance in City-Scale Multi-Camera Vehicle Re-Identification demonstrated the advantage of our methods, and we got 5-th place in the vehicle Re-ID track of AIC2020.
Communication-Efficient Edge AI Inference Over Wireless Networks
Given the fast growth of intelligent devices, it is expected that a large number of high-stake artificial intelligence (AI) applications, e. g., drones, autonomous cars, tactile robots, will be deployed at the edge of wireless networks in the near future.
Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface
Intelligent Internet-of-Things (IoT) will be transformative with the advancement of artificial intelligence and high-dimensional data analysis, shifting from "connected things" to "connected intelligence".
Facial Action Unit Detection via Adaptive Attention and Relation
Specifically, we propose an adaptive attention regression network to regress the global attention map of each AU under the constraint of attention predefinition and the guidance of AU detection, which is beneficial for capturing both specified dependencies by landmarks in strongly correlated regions and facial globally distributed dependencies in weakly correlated regions.
Video Object Segmentation and Tracking: A Survey
Second, we provide a detailed discussion and overview of the technical characteristics of the different methods.
iQIYI-VID: A Large Dataset for Multi-modal Person Identification
Multi-modal person identification is a more promising way that we can jointly utilize face, head, body, audio features, and so on.
Chinese Grammatical Error Diagnosis Based on CRF and LSTM-CRF model
When learning Chinese as a foreign language, the learners may have some grammatical errors due to negative migration of their native languages.
A novel total variation model based on kernel functions and its application
In this novel model, we first map each pixel value of an image into a Hilbert space by using a nonlinear map, and then define a coupled image of an original image in order to construct a kernel function.
