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Found 13 papers, 5 papers with code
AI-driven 6G Air Interface: Technical Usage Scenarios and Balanced Design Methodology
This paper systematically analyzes the typical application scenarios and key technical challenges of AI in 6G air interface transmission, covering important areas such as performance enhancement of single functional modules, joint optimization of multiple functional modules, and low-complexity solutions to complex mathematical problems.
Path Evolution Model for Endogenous Channel Digital Twin towards 6G Wireless Networks
Thirdly, environmental generalizability can greatly reduce deployment costs in dynamic environments.
Energy Optimization of Multi-task DNN Inference in MEC-assisted XR Devices: A Lyapunov-Guided Reinforcement Learning Approach
In response to the challenges posed by the high energy consumption and limited resources of XR devices, we designed a dual time-scale joint optimization strategy for model partitioning and resource allocation, formulated as a bi-level optimization problem.
Hard-Label Cryptanalytic Extraction of Neural Network Models
However, this goal is not achieved when neural networks operate under a hard-label setting where the raw output is inaccessible.
Have You Merged My Model? On The Robustness of Large Language Model IP Protection Methods Against Model Merging
Model merging is a promising lightweight model empowerment technique that does not rely on expensive computing devices (e. g., GPUs) or require the collection of specific training data.
Wireless Network Digital Twin for 6G: Generative AI as A Key Enabler
Digital twin, which enables emulation, evaluation, and optimization of physical entities through synchronized digital replicas, has gained increasing attention as a promising technology for intricate wireless networks.
FigStep: Jailbreaking Large Vision-Language Models via Typographic Visual Prompts
Large Vision-Language Models (LVLMs) signify a groundbreaking paradigm shift within the Artificial Intelligence (AI) community, extending beyond the capabilities of Large Language Models (LLMs) by assimilating additional modalities (e. g., images).
Toward 6G Native-AI Network: Foundation Model based Cloud-Edge-End Collaboration Framework
Then, we propose a 6G native AI framework based on foundation models, provide an integration method for the expert knowledge, present the customization for two kinds of PFM, and outline a novel operational paradigm for the native AI framework.
Multi-task Learning-based CSI Feedback Design in Multiple Scenarios
One efficient CSI feedback method is the Auto-Encoder (AE) structure based on deep learning, yet facing problems in actual deployments, such as selecting the deployment mode when deploying in a cell with multiple complex scenarios.
Energy-based Out-of-distribution Detection
We propose a unified framework for OOD detection that uses an energy score.
GraphDefense: Towards Robust Graph Convolutional Networks
Inspired by the previous works on adversarial defense for deep neural networks, and especially adversarial training algorithm, we propose a method called GraphDefense to defend against the adversarial perturbations.
Attack Graph Convolutional Networks by Adding Fake Nodes
In this paper, we propose a new type of "fake node attacks" to attack GCNs by adding malicious fake nodes.
Phoneme Set Design Using English Speech Database by Japanese for Dialogue-Based English CALL Systems
This paper describes a method of generating a reduced phoneme set for dialogue-based computer assisted language learning (CALL)systems.
