Search Results for author:
Found 31 papers, 8 papers with code
ALWNN Empowered Automatic Modulation Classification: Conquering Complexity and Scarce Sample Conditions
The ALWNN model, by integrating the adaptive wavelet neural network and depth separable convolution, reduces the number of model parameters and computational complexity.
Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey
To achieve high reliability and scalability in deploying this paradigm in distributed systems, it is essential to conquer challenges stemming from both spatial and temporal dimensions, manifesting as distribution shifts, catastrophic forgetting, heterogeneity, and privacy issues.
Deploying Foundation Model Powered Agent Services: A Survey
Foundation model (FM) powered agent services are regarded as a promising solution to develop intelligent and personalized applications for advancing toward Artificial General Intelligence (AGI).
Boosting Fine-Grained Visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial Learning
Then, a coarse-knowledge-aware adversarial learning method is developed to align the distribution of reconstructed features with that of normal features.
A QoE-Aware Split Inference Accelerating Algorithm for NOMA-based Edge Intelligence
Thus, the model split inference is proposed to improve the performance of edge intelligence, in which the AI model is divided into different sub models and the resource-intensive sub model is offloaded to edge server wirelessly for reducing resource requirements and inference latency.
GNN-Empowered Effective Partial Observation MARL Method for AoI Management in Multi-UAV Network
Due to the discretization and temporal features of AoI indicators, the Qedgix framework employs QMIX to optimize distributed partially observable Markov decision processes (Dec-POMDP) based on centralized training and distributed execution (CTDE) with respect to mean AoI values of users.
FedBAT: Communication-Efficient Federated Learning via Learnable Binarization
To this end, we propose Federated Binarization-Aware Training (FedBAT), a novel framework that directly learns binary model updates during the local training process, thus inherently reducing the approximation errors.
Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models
While Large Vision-Language Models (LVLMs) have rapidly advanced in recent years, the prevalent issue known as the `hallucination' problem has emerged as a significant bottleneck, hindering their real-world deployments.
Detached and Interactive Multimodal Learning
Recently, Multimodal Learning (MML) has gained significant interest as it compensates for single-modality limitations through comprehensive complementary information within multimodal data.
ST-Mamba: Spatial-Temporal Mamba for Traffic Flow Estimation Recovery using Limited Data
Traffic flow estimation (TFE) is crucial for urban intelligent traffic systems.
Spatial-Temporal Attention Model for Traffic State Estimation with Sparse Internet of Vehicles
The growing number of connected vehicles offers an opportunity to leverage internet of vehicles (IoV) data for traffic state estimation (TSE) which plays a crucial role in intelligent transportation systems (ITS).
Personalized Federated Domain-Incremental Learning based on Adaptive Knowledge Matching
Then, the client can choose to adopt a new initial model or a previous model with similar knowledge to train the new task and simultaneously migrate knowledge from previous tasks based on these correlations.
Disentangle Estimation of Causal Effects from Cross-Silo Data
Estimating causal effects among different events is of great importance to critical fields such as drug development.
C2KD: Bridging the Modality Gap for Cross-Modal Knowledge Distillation
We empirically reveal that the modality gap i. e. modality imbalance and soft label misalignment incurs the ineffectiveness of traditional KD in CMKD.
Balanced Multi-modal Federated Learning via Cross-Modal Infiltration
Federated learning (FL) underpins advancements in privacy-preserving distributed computing by collaboratively training neural networks without exposing clients' raw data.
Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selection
On the other hand, we propose the modality selection aiming to select subsets of local modalities with great diversity and achieving global modal balance simultaneously.
Mobility and Cost Aware Inference Accelerating Algorithm for Edge Intelligence
The model segmentation without user mobility has been investigated deeply by previous works.
MDENet: Multi-modal Dual-embedding Networks for Malware Open-set Recognition
Malware open-set recognition (MOSR) aims at jointly classifying malware samples from known families and detect the ones from novel unknown families, respectively.
Towards Unbiased Training in Federated Open-world Semi-supervised Learning
Federated Semi-supervised Learning (FedSSL) has emerged as a new paradigm for allowing distributed clients to collaboratively train a machine learning model over scarce labeled data and abundant unlabeled data.
Non-Exemplar Online Class-incremental Continual Learning via Dual-prototype Self-augment and Refinement
In this paper, we propose a novel Dual-prototype Self-augment and Refinement method (DSR) for NO-CL problem, which consists of two strategies: 1) Dual class prototypes: vanilla and high-dimensional prototypes are exploited to utilize the pre-trained information and obtain robust quasi-orthogonal representations rather than example buffers for both privacy preservation and memory reduction.
DualMix: Unleashing the Potential of Data Augmentation for Online Class-Incremental Learning
Unfortunately, OCI learning can suffer from catastrophic forgetting (CF) as the decision boundaries for old classes can become inaccurate when perturbated by new ones.
RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous Driving
Then we implement muti-type sensor detection and multi-group sensors fusion in this environment, including camera-radar and camera-lidar detection based on result-level fusion.
DaFKD: Domain-Aware Federated Knowledge Distillation
In this paper, we propose a new perspective that treats the local data in each client as a specific domain and design a novel domain knowledge aware federated distillation method, dubbed DaFKD, that can discern the importance of each model to the distillation sample, and thus is able to optimize the ensemble of soft predictions from diverse models.
AirCon: Over-the-Air Consensus for Wireless Blockchain Networks
Blockchain has been deemed as a promising solution for providing security and privacy protection in the next-generation wireless networks.
ProCC: Progressive Cross-primitive Compatibility for Open-World Compositional Zero-Shot Learning
Open-World Compositional Zero-shot Learning (OW-CZSL) aims to recognize novel compositions of state and object primitives in images with no priors on the compositional space, which induces a tremendously large output space containing all possible state-object compositions.
FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers
Federated Learning (FL) is an emerging paradigm that enables distributed users to collaboratively and iteratively train machine learning models without sharing their private data.
PMR: Prototypical Modal Rebalance for Multimodal Learning
Multimodal learning (MML) aims to jointly exploit the common priors of different modalities to compensate for their inherent limitations.
Demystify Self-Attention in Vision Transformers from a Semantic Perspective: Analysis and Application
In contrast, visual data exhibits a fundamentally different structure: Its basic unit (pixel) is a natural low-level representation with significant redundancies in the neighbourhood, which poses obvious challenges to the interpretability of MSA mechanism in ViT.
SigT: An Efficient End-to-End MIMO-OFDM Receiver Framework Based on Transformer
Multiple-input multiple-output and orthogonal frequency-division multiplexing (MIMO-OFDM) are the key technologies in 4G and subsequent wireless communication systems.
PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models -- Federated Learning in Age of Foundation Model
Quick global aggregation of effective distributed parameters is crucial to federated learning (FL), which requires adequate bandwidth for parameters communication and sufficient user data for local training.
Efficient Attribute Unlearning: Towards Selective Removal of Input Attributes from Feature Representations
Existing studies of machine unlearning mainly focus on sample-wise unlearning, such that a learnt model will not expose user's privacy at the sample level.
