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Found 17 papers, 4 papers with code
Length Controlled Generation for Black-box LLMs
Large language models (LLMs) have demonstrated impressive instruction following capabilities, while still struggling to accurately manage the length of the generated text, which is a fundamental requirement in many real-world applications.
A Semantic Communication System for Real-time 3D Reconstruction Tasks
Specifically, we design an encoding-decoding semantic communication framework for real-time semantic mapping tasks under limited-resource situations.
Discrete Modeling via Boundary Conditional Diffusion Processes
Experimental results indicate that our approach achieves strong performance in both language modeling and discrete image generation tasks.
BFA-YOLO: A balanced multiscale object detection network for building façade attachments detection
To address these issues, we develop the BFA-YOLO model and the BFA-3D dataset in this study.
BeamAggR: Beam Aggregation Reasoning over Multi-source Knowledge for Multi-hop Question Answering
For composite questions, the LLM combines beam candidates, explores multiple reasoning paths through probabilistic aggregation, and prioritizes the most promising trajectory.
An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation
Retrieval-augmented generation integrates the capabilities of large language models with relevant information retrieved from an extensive corpus, yet encounters challenges when confronted with real-world noisy data.
Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities
For that, this paper aims to provide a comprehensive survey on applications, challenges, and opportunities of GAI in unmanned vehicle swarms.
Learning to Break: Knowledge-Enhanced Reasoning in Multi-Agent Debate System
First, we involve a shared retrieval knowledge pool in the debate process to solve the problem of limited and different knowledge backgrounds.
Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images
In this work, we aim to utilize the strong visual recognition capabilities of VFMs to improve the change detection of high-resolution Remote Sensing Images (RSIs).
Leveraging Optical Communication Fiber and AI for Distributed Water Pipe Leak Detection
Detecting leaks in water networks is a costly challenge.
Hierarchical Catalogue Generation for Literature Review: A Benchmark
Scientific literature review generation aims to extract and organize important information from an abundant collection of reference papers and produces corresponding reviews while lacking a clear and logical hierarchy.
Compressed domain vibration detection and classification for distributed acoustic sensing
Distributed acoustic sensing (DAS) is a novel enabling technology that can turn existing fibre optic networks to distributed acoustic sensors.
Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective
In this paper, we provide a comprehensive review for the economic and game theoretic approaches proposed in the literature to design various schemes for stimulating data owners to participate in FL training process.
Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey
Then, we review a number of applications of MARL to solve emerging issues in future Internet.
Co-Imitation Learning without Expert Demonstration
Imitation learning is a primary approach to improve the efficiency of reinforcement learning by exploiting the expert demonstrations.
Dynamic Network Service Selection in Intelligent Reflecting Surface-Enabled Wireless Systems: Game Theory Approaches
To formulate the SP and network service selection, we adopt an evolutionary game in which the users are able to adapt their network selections depending on the utilities that they achieve.
Computer Science and Game Theory
Data-Driven Machine Learning Techniques for Self-healing in Cellular Wireless Networks: Challenges and Solutions
As an important component in SON, self-healing is defined as a network paradigm where the faults of target networks are mitigated or recovered by automatically triggering a series of actions such as detection, diagnosis and compensation.
