specific knowledge. Additionally, the experimental studies conducted on four different datasets demonstrate that the explanation graph generated by GNNInterpreter can match the desired graph pattern when the model is ideal and reveal potential model pitfalls if there exist any. https://openreview.net/for...
before all partial responses are again summarized in a final response to the user. For a class of global sensemaking questions over datasets in the 1 million token range, we show that Graph RAG leads to substantial improvements over a na¨ıve RAG baseline for both the comprehensiveness and...
computer-vision deep-learning optimization probability deep-reinforcement-learning medical-imaging speech-recognition artificial-neural-networks pattern-recognition probabilistic-graphical-models bayesian-statistics artificial-intelligence-algorithms visual-recognition geometric-deep-learning explainable-ai graph-neural-...
Generative AI for Strategy & Innovation: an experiment about management theories with ChatGPT by Harvard Business Review Italia The TextFX project: "AI-powered tools for rappers, writers and wordsmiths" (partnership between Lupe Fiasco and Google) A jargon-free explanation of how AI large language...
The TextGraphs-13 Shared Task on Explanation Regeneration asked participants to develop methods to reconstruct gold explanations for elementary science questions. Red Dragon AI's entries used the language of the questions and explanation text directly, rather than a constructing a separate graph-like re...
and one of the challenges that has been in the field for a long time is that we have great methods for estimating the causal effect once we have the graph established, but getting that graph often is a really challenging process, and you need to get domain expertise, huma...
[5] Ribeiro, M. T.; Singh, S.; and Guestrin, C. 2016. “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–44. New York: Association for Computing Machinery...
6. 【Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing】 本文提出了一种基于解释引导的公平联邦学习(EGFL)方法,用于实现透明可信的6G网络切片的零触碰服务管理。该方法在全面性、收敛性和公平性方面具有优势。文章引用了其他相关研究,包括关于引导注意力推理网络、解释引导学习、预测5G服务导向...
The graph can be learned via auto-encoding to ensure self-consistency, e.g. (Bear et al., 2020). 相对于个体只能来说,群体智能还需要有效地沟通和知识的交流: We suggest that new principles for the emergence of high-level intelligence, if any, should be sought through the need for efficient...
KeybindExplanation Ctrl + Enter Queue up current graph for generation将当前图形排队以供生成 Ctrl + Shift + Enter Queue up current graph as first for generation将当前图形队列为生成的第一个图形 Ctrl + Z/Ctrl + Y Undo/Redo Ctrl + S Save workflow 保存工作流 Ctrl + O Load workflow 加载工作流...