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Found 9 papers, 1 papers with code
GenAINet: Enabling Wireless Collective Intelligence via Knowledge Transfer and Reasoning
Connecting GenAI agents over a wireless network can potentially unleash the power of collective intelligence and pave the way for artificial general intelligence (AGI).
Multimodal Transformers for Wireless Communications: A Case Study in Beam Prediction
Wireless communications at high-frequency bands with large antenna arrays face challenges in beam management, which can potentially be improved by multimodality sensing information from cameras, LiDAR, radar, and GPS.
Joint Semantic-Native Communication and Inference via Minimal Simplicial Structures
Specifically, the teacher first maps its data into a k-order simplicial complex and learns its high-order correlations.
Large Generative AI Models for Telecom: The Next Big Thing?
To be specific, large GenAI models are envisioned to open up a new era of autonomous wireless networks, in which multi-modal GenAI models trained over various Telecom data, can be fine-tuned to perform several downstream tasks, eliminating the need for building and training dedicated AI models for each specific task and paving the way for the realization of artificial general intelligence (AGI)-empowered wireless networks.
Understanding Telecom Language Through Large Language Models
In particular, we fine-tune several LLMs including BERT, distilled BERT, RoBERTa and GPT-2, to the Telecom domain languages, and demonstrate a use case for identifying the 3rd Generation Partnership Project (3GPP) standard working groups.
Semantic-Native Communication: A Simplicial Complex Perspective
In this paper, we study semantic communication from a topological space perspective, in which higher-order data semantics live in a simplicial complex.
In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning
In order to bring more intelligence to the edge systems, compared to traditional optimization methodology, and driven by the current deep learning techniques, we propose to integrate the Deep Reinforcement Learning techniques and Federated Learning framework with the mobile edge systems, for optimizing the mobile edge computing, caching and communication.
Better Image Segmentation by Exploiting Dense Semantic Predictions
The paper focuses on utilizing the FCNN-based dense semantic predictions in the bottom-up image segmentation, arguing to take semantic cues into account from the very beginning.
Suppressing the Unusual: towards Robust CNNs using Symmetric Activation Functions
These units suppress signals of exceptional magnitude.
