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Found 37 papers, 11 papers with code
Digital Semantic Communications: An Alternating Multi-Phase Training Strategy with Mask Attack
In particular, in the FE stage, we learn the representation ability of semantic information by end-to-end training the encoder and decoder in an analog manner.
Bias-Aware Low-Rank Adaptation: Mitigating Catastrophic Inheritance of Large Language Models
Large language models (LLMs) have exhibited remarkable proficiency across a diverse array of natural language processing (NLP) tasks.
Language Models can Evaluate Themselves via Probability Discrepancy
In this paper, we initiate our discussion by demonstrating how Large Language Models (LLMs), when tasked with responding to queries, display a more even probability distribution in their answers if they are more adept, as opposed to their less skilled counterparts.
NegativePrompt: Leveraging Psychology for Large Language Models Enhancement via Negative Emotional Stimuli
The results are revealing: NegativePrompt markedly enhances the performance of LLMs, evidenced by relative improvements of 12. 89% in Instruction Induction tasks and 46. 25% in BIG-Bench tasks.
You Only Need Half: Boosting Data Augmentation by Using Partial Content
We propose a novel data augmentation method termed You Only Need hAlf (YONA), which simplifies the augmentation process.
Vision Transformer-based Adversarial Domain Adaptation
Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain.
Margin Discrepancy-based Adversarial Training for Multi-Domain Text Classification
Subsequently, we propose a margin discrepancy-based adversarial training (MDAT) approach for MDTC, in accordance with our theoretical analysis.
Multi-Scale Semantic Segmentation with Modified MBConv Blocks
Recently, MBConv blocks, initially designed for efficiency in resource-limited settings and later adapted for cutting-edge image classification performances, have demonstrated significant potential in image classification tasks.
A Survey on Data Augmentation in Large Model Era
Leveraging large models, these data augmentation techniques have outperformed traditional approaches.
PoseIRM: Enhance 3D Human Pose Estimation on Unseen Camera Settings via Invariant Risk Minimization
A natural solution is to artificially synthesize some samples i. e. 2D-3D pose pairs under massive new camera settings.
Regularized Conditional Alignment for Multi-Domain Text Classification
The most successful multi-domain text classification (MDTC) approaches employ the shared-private paradigm to facilitate the enhancement of domain-invariant features through domain-specific attributes.
Deep Image Semantic Communication Model for Artificial Intelligent Internet of Things
Particularly, at the transmitter side, a high-precision image semantic segmentation algorithm is proposed to extract the semantic information of the image to achieve significant compression of the image data.
Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices
Distributed Artificial Intelligence (AI) model training over mobile edge networks encounters significant challenges due to the data and resource heterogeneity of edge devices.
Intelligent Reflecting Surface Aided Multi-Tier Hybrid Computing
Moreover, the results indicate that the DT assisted MEC system can precisely achieve the balance between local computing and task offloading since real-time system status can be obtained with the help of DT.
Service Reservation and Pricing for Green Metaverses: A Stackelberg Game Approach
A single-leader multi-follower Stackelberg game is formulated between the MSP and users while each user optimizes an offloading probability to minimize the weighted sum of time, energy consumption and monetary cost.
Federated Learning-Empowered AI-Generated Content in Wireless Networks
Artificial intelligence generated content (AIGC) has emerged as a promising technology to improve the efficiency, quality, diversity and flexibility of the content creation process by adopting a variety of generative AI models.
A Survey on Evaluation of Large Language Models
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications.
AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices
We propose a cost-adjustable FL framework, named AnycostFL, that enables diverse edge devices to efficiently perform local updates under a wide range of efficiency constraints.
DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-shot Learning
In this paper, we propose a Domain-Agnostic and Sample-Efficient wireless indoor crowd Counting (DASECount) framework that suffices to attain robust cross-domain detection accuracy given very limited data samples in new domains.
IRS Assisted NOMA Aided Mobile Edge Computing with Queue Stability: Heterogeneous Multi-Agent Reinforcement Learning
In this paper, we investigate the joint offloading, communication and computation resource allocation for IRS-assisted NOMA MEC system.
SRPCN: Structure Retrieval based Point Completion Network
Besides, the missing patterns are diverse in reality, but existing methods can only handle fixed ones, which means a poor generalization ability.
Co-Regularized Adversarial Learning for Multi-Domain Text Classification
Multi-domain text classification (MDTC) aims to leverage all available resources from multiple domains to learn a predictive model that can generalize well on these domains.
Maximum Batch Frobenius Norm for Multi-Domain Text Classification
Multi-domain text classification (MDTC) has obtained remarkable achievements due to the advent of deep learning.
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.
Attention-based Transformation from Latent Features to Point Clouds
The points generated by AXform do not have the strong 2-manifold constraint, which improves the generation of non-smooth surfaces.
FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing
In PVEC, different PLOs recruit PVs as edge computing nodes for offloading services through an incentive mechanism, which is designed according to the computation demand and parking capacity constraints derived from FedParking.
Towards Category and Domain Alignment: Category-Invariant Feature Enhancement for Adversarial Domain Adaptation
Adversarial domain adaptation has made impressive advances in transferring knowledge from the source domain to the target domain by aligning feature distributions of both domains.
Conditional Adversarial Networks for Multi-Domain Text Classification
We provide theoretical analysis for the CAN framework, showing that CAN's objective is equivalent to minimizing the total divergence among multiple joint distributions of shared features and label predictions.
Mixup Regularized Adversarial Networks for Multi-Domain Text Classification
Using the shared-private paradigm and adversarial training has significantly improved the performances of multi-domain text classification (MDTC) models.
Dual Adversarial Training for Unsupervised Domain Adaptation
Domain adaptation sets out to address this problem, aiming to leverage labeled data in the source domain to learn a good predictive model for the target domain whose labels are scarce or unavailable.
Deep Reinforcement Learning-Based Dynamic Resource Management for Mobile Edge Computing in Industrial Internet of Things
In order to minimize the long-term average delay of the tasks, theoriginal problem is transformed into a Markov decision process (MDP).
Dual Mixup Regularized Learning for Adversarial Domain Adaptation
Second, samples from the source and target domains alone are not sufficient for domain-invariant feature extracting in the latent space.
Visualizing Deep Learning-based Radio Modulation Classifier
Extensive numerical results show both the CNN-based classifier and LSTM-based classifier extract similar radio features relating to modulation reference points.
Estimating Uncertainty Intervals from Collaborating Networks
We address these challenges by proposing a novel method to capture predictive distributions in regression by defining two neural networks with two distinct loss functions.
Data Augmentation for Deep Learning-based Radio Modulation Classification
Deep learning has recently been applied to automatically classify the modulation categories of received radio signals without manual experience.
Dual Adversarial Co-Learning for Multi-Domain Text Classification
In this paper we propose a novel dual adversarial co-learning approach for multi-domain text classification (MDTC).
General Classification
Multi-Domain Sentiment Classification
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Chi-Square Test Neural Network: A New Binary Classifier based on Backpropagation Neural Network
We introduce the chi-square test neural network: a single hidden layer backpropagation neural network using chi-square test theorem to redefine the cost function and the error function.
