Titlebook: Artificial Intelligence. ECAI 2023 International Workshops; XAI^3, TACTIFUL, XI- S?awomir Nowaczyk,Przemys?aw Biecek,Vania Dimitrov Confere

DOTE

Adjourn

暫時(shí)過來

不安

Temporal Saliency Detection Towards Explainable Transformer-Based Timeseries Forecastingllenge, especially towards explainability. Focusing on commonly used saliency maps in explaining DNN in general, our quest is to build attention-based architecture that can automatically encode saliency-related temporal patterns by establishing connections with appropriate attention heads. Hence, th

欄桿

Explaining Taxi Demand Prediction Models Based on?Feature Importanceem, which is difficult due to its multivariate input and output space. As these models are composed of multiple layers, their predictions become opaque. This opaqueness makes debugging, optimising, and using the models difficult. To address this, we propose the usage of eXplainable AI (XAI) – featur

消息靈通

Bayesian CAIPI: A Probabilistic Approach to?Explanatory and?Interactive Machine Learningart algorithm, captures the user feedback and iteratively biases a data set toward a correct decision-making mechanism using counterexamples. The counterexample generation procedure relies on hand-crafted data augmentation and might produce implausible instances. We propose Bayesian CAIPI that embed

Gleason-score

啪心兒跳動(dòng)

A. M. Gaines,B. A. Peterson,O. F. Mendoza augment the predictive capabilities of hypercube-based SKE techniques, striving for a completeness rate of 100%. Furthermore, the study includes experiments that assess the effectiveness of the proposed enhancements.

Innovative

https://doi.org/10.1007/978-3-319-76864-9 ability to generate such surrogate models. We investigate fidelity, interpretability, stability, and the algorithms’ capability to capture interaction effects through appropriate splits. Based on our comprehensive analyses, we finally provide an overview of user-specific recommendations.

coagulation

https://doi.org/10.1007/978-3-319-76321-7where we distinguish ones from sevens, we show that Bayesian CAIPI matches the predictive accuracy of both, traditional CAIPI and default deep learning. Moreover, it outperforms both in terms of explanation quality.