Full Bayesian Significance Testing for Neural Networks

Full Bayesian Significance Testing for Neural Networks

AAAI Conference on Artificial Intelligence (AAAI), 2024

24 January 2024

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Papers citing "Full Bayesian Significance Testing for Neural Networks"

6 / 6 papers shown

Title
Simple and Efficient Heterogeneous Temporal Graph Neural Network Yili Wang Tairan Huang Changlong He Qiutong Li Jianliang Gao AI4TS 40 0 0 21 Oct 2025
ProDiff: Prototype-Guided Diffusion for Minimal Information Trajectory Imputation Tianci Bu Le Zhou Wenchuan Yang Jianhong Mou Kang Yang Suoyi Tan Feng Yao Jingyuan Wang Xin Lu 132 0 0 29 May 2025
Learning Universal Multi-level Market Irrationality Factors to Improve Stock Return ForecastingKnowledge Discovery and Data Mining (KDD), 2025 Chen Yang Jingyuan Wang Xiaohan Jiang Junjie Wu AIFin AI4TS 173 1 0 07 Feb 2025
BIGCity: A Universal Spatiotemporal Model for Unified Trajectory and Traffic State Data AnalysisIEEE International Conference on Data Engineering (ICDE), 2024 Xie Yu Jingyuan Wang Yifan Yang Qian Huang Ke Qu 206 11 0 01 Dec 2024
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.9K 26,957 0 22 May 2017
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.7K 18,692 0 16 Feb 2016

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