Toward green and human-like artificial intelligence: A complete survey on contemporary few-shot learning approaches

G Tsoumplekas, V Li, V Argyriou, A Lytos… - arXiv preprint arXiv …, 2024 - arxiv.org
Despite deep learning's widespread success, its data-hungry and computationally
expensive nature makes it impractical for many data-constrained real-world applications …

Embodied AI-Enhanced Vehicular Networks: An Integrated Large Language Models and Reinforcement Learning Method

R Zhang, C Zhao, H Du, D Niyato, J Wang… - arXiv preprint arXiv …, 2025 - arxiv.org
This paper investigates adaptive transmission strategies in embodied AI-enhanced
vehicular networks by integrating large language models (LLMs) for semantic information …

Neuromodulated Meta-Learning

J Wang, H Guo, W Qiang, J Li, C Zheng… - arXiv preprint arXiv …, 2024 - arxiv.org
Humans excel at adapting perceptions and actions to diverse environments, enabling
efficient interaction with the external world. This adaptive capability relies on the biological …

[图书][B] Meta-Reinforcement Learning: Algorithms and Applications

EZ Liu - 2023 - search.proquest.com
Reinforcement learning from scratch often requires a tremendous number of samples to
learn complex tasks, but many real-world applications demand learning from only a few …

A Combinatorial Approach to Neural Emergent Communication

Z Zhang - arXiv preprint arXiv:2410.18806, 2024 - arxiv.org
Substantial research on deep learning-based emergent communication uses the referential
game framework, specifically the Lewis signaling game, however we argue that successful …

Conversational agents in human-machine interaction: reinforcement learning and theory of mind in language modeling

N Brandizzi - 2024 - iris.uniroma1.it
This doctoral thesis addresses the challenges and advancements in the realm of Human-
Machine Interaction, specifically focusing on the agency and misalignment of modern Large …