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Found 28 papers, 0 papers with code
Discrete Codebook Design for Self-interference Suppression in mmWave ISAC
Under an alternating optimization framework, the RX and TX codewords are iteratively optimized, with one fixed while the other is optimized.
Drift-Aware Federated Learning: A Causal Perspective
This paper examine the relationship between model update drift and global as well as local optimizer from causal perspective.
Efficient UAV Swarm-Based Multi-Task Federated Learning with Dynamic Task Knowledge Sharing
Through theoretical analysis, we identify key factors affecting task performance and introduce a task attention mechanism to dynamically evaluate task importance, thereby achieving efficient resource allocation.
Invariant Federated Learning for Edge Intelligence: Mitigating Heterogeneity and Asynchrony via Exit Strategy and Invariant Penalty
It is proved in this paper that the exit of abnormal edge clients can guarantee the effect of the model on most clients.
The Impact Analysis of Delays in Asynchronous Federated Learning with Data Heterogeneity for Edge Intelligence
This paper examines the impact of unknown causes of delay on training performance in an Asynchronous Federated Learning (AFL) system with data heterogeneity.
MAB-Based Channel Scheduling for Asynchronous Federated Learning in Non-Stationary Environments
To address these challenges, we propose an asynchronous federated learning scheduling framework for non-stationary channel environments to reduce staleness while promoting fair and efficient communication and aggregation. We focus on two channel scenarios: extremely non-stationary and piecewise stationary.
Temporal Misalignment and Probabilistic Neurons
Spiking Neural Networks (SNNs) offer a more energy-efficient alternative to Artificial Neural Networks (ANNs) by mimicking biological neural principles, establishing them as a promising approach to mitigate the increasing energy demands of large-scale neural models.
UAV-Assisted Multi-Task Federated Learning with Task Knowledge Sharing
The rapid development of Unmanned aerial vehicles (UAVs) technology has spawned a wide variety of applications, such as emergency communications, regional surveillance, and disaster relief.
The Impact of Visual Information in Chinese Characters: Evaluating Large Models' Ability to Recognize and Utilize Radicals
The glyphic writing system of Chinese incorporates information-rich visual features in each character, such as radicals that provide hints about meaning or pronunciation.
GSD: View-Guided Gaussian Splatting Diffusion for 3D Reconstruction
We present GSD, a diffusion model approach based on Gaussian Splatting (GS) representation for 3D object reconstruction from a single view.
FTBC: Forward Temporal Bias Correction for Optimizing ANN-SNN Conversion
Spiking Neural Networks (SNNs) offer a promising avenue for energy-efficient computing compared with Artificial Neural Networks (ANNs), closely mirroring biological neural processes.
A Simulation-Augmented Benchmarking Framework for Automatic RSO Streak Detection in Single-Frame Space Images
In our framework, by making the best use of the hardware parameters of the sensor that captures real-world space images, we first develop a high-fidelity RSO simulator that can generate various realistic space images.
Efficient 3D Object Reconstruction using Visual Transformers
Reconstructing a 3D object from a 2D image is a well-researched vision problem, with many kinds of deep learning techniques having been tried.
Densely Semantic Enhancement for Domain Adaptive Region-free Detectors
Unsupervised domain adaptive object detection aims to adapt a well-trained detector from its original source domain with rich labeled data to a new target domain with unlabeled data.
A Model-driven and Data-driven Fusion Framework for Accurate Air Quality Prediction
Then, we deploy a long-short-term-memory (LSTM) to fetch the preliminary results, which will be further corrected by a neural network (NN) involving the meteorological index as well as other pollutants concentrations.
Deep Reinforcement Learning for Multi-Resource Multi-Machine Job Scheduling
Minimizing job scheduling time is a fundamental issue in data center networks that has been extensively studied in recent years.
Fast Gated Neural Domain Adaptation: Language Model as a Case Study
We show that the neural LM perplexity can be reduced by 7. 395 and 12. 011 using the proposed domain adaptation mechanism on the Penn Treebank and News data, respectively.
ProphetMT: A Tree-based SMT-driven Controlled Language Authoring/Post-Editing Tool
This paper presents ProphetMT, a tree-based SMT-driven Controlled Language (CL) authoring and post-editing tool.
Improve the Evaluation of Fluency Using Entropy for Machine Translation Evaluation Metrics
The widely-used automatic evaluation metrics cannot adequately reflect the fluency of the translations.
An Automatic Machine Translation Evaluation Metric Based on Dependency Parsing Model
To avoid these problems, we propose a novel automatic evaluation metric based on dependency parsing model, with no need to define sub-structures by human.
