Agent-Based Models (ABMs) are used in several fields to study the evolution of complex systems from micro-level assumptions. However, a significant drawback of ABMs is their inability to estimate agent-specific (or “micro”) variables, which hinders their ability to make accurate predictions ...
To optimize the search process, mathematical models of lost person behavior with respect to landscape can be used in conjunction with current SAR practices. In this paper, we introduce an agent-based model of lost person behavior which allows agents to move on known landscapes with behavior ...
Agent-based models tend to seek explanatory insight into the collective behavior of agents (often real-world organisms or systems), whereas multi-agent systems tend to be more focused on solving practical or engineering problems through optimization of the design of agents. Insights from this topic...
How simple models can reach the performance of complex models Unsupervised data pruning: less data to learn better -- Not always more data is meaning a more accurate model, but how to choose your data? Back to General index -- Index of tutorials Tabular learning Articlesnotebookdescription ...
[1] Yujia Qin, Shengding Hu, Yankai Lin, Weize Chen, Ning Ding, Ganqu Cui, Zheni Zeng, YufeiHuang, Chaojun Xiao, Chi Han, et al. Tool learning with foundation models. arXiv preprintarXiv:2304.08354, 2023. 总结了使大语言模型使用外部扩展工具的先进方法。
As for the second aspect, we summarize the strategies for agent capability acquisition based on whether they fine-tune the LLMs. When comparing LLM-based autonomous agents to traditional machine learning, designing the agent architecture is analogous to determining the network structure, while the ...
On average, models trained in a joint learning mode are more likely to label short utterances. Table 4. Quantitative summarization results on the SAMSum and DialogSum test sets after fine-tuning (3-runs average). Top: abstractive baselines. Bottom: backbone vanilla models and our annotate-then-...
On the other hand, multi-agent deep reinforcement learning is a powerful technique that combines multiple models or agents that can adapt to different market conditions and learn from each other. This can lead to more robust and effective trading strategies that can generate higher profits and ...
This is the first work to correct hallucination in multimodal large language models. ✨ Table of Contents Awesome Papers Multimodal Instruction Tuning Multimodal Hallucination Multimodal In-Context Learning Multimodal Chain-of-Thought LLM-Aided Visual Reasoning ...
Machine Learning for NLP A Comprehensive Survey on Word Representation Models: From Classical to State-Of-The-Art Word Representation Language Models. ACM Trans. Asian Low Resour. Lang. Inf. Process. 2021 paper bib Usman Naseem, Imran Razzak, Shah Khalid Khan, Mukesh Prasad A Survey Of Cross...