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Found 9 papers, 6 papers with code
Towards Non-Adversarial Algorithmic Recourse
We subsequently investigate how different components in the objective functions, e. g., the machine learning model or cost function used to measure distance, determine whether the outcome can be considered an adversarial example or not.
Robust Stochastic Graph Generator for Counterfactual Explanations
Counterfactual Explanation (CE) techniques have garnered attention as a means to provide insights to the users engaging with AI systems.
Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes
The GAEs minimise the reconstruction error between the original graph and its learned representation during training.
Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection
Leading OCC techniques constrain the latent representations of normal motions to limited volumes and detect as abnormal anything outside, which accounts satisfactorily for the openset'ness of anomalies.
Ranked #1 on
Video Anomaly Detection
on HR-UBnormal
Unsupervised Detection of Behavioural Drifts with Dynamic Clustering and Trajectory Analysis
Real-time monitoring of human behaviours, especially in e-Health applications, has been an active area of research in the past decades.
Agnostic Visual Recommendation Systems: Open Challenges and Future Directions
Visualization Recommendation Systems (VRSs) are a novel and challenging field of study aiming to help generate insightful visualizations from data and support non-expert users in information discovery.
Are we certain it's anomalous?
By using uncertainty, HypAD may assess whether it is certain about the input signal but it fails to reconstruct it because this is anomalous; or whether the reconstruction error does not necessarily imply anomaly, as the model is uncertain, e. g. a complex but regular input signal.
A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation
Due to the growing attention in graph learning, we focus on the concepts of CE for GNNs.
Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection
Autoencoders, as a dimensionality reduction technique, have been recently applied to outlier detection.
