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Found 6 papers, 3 papers with code
MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme
To address these challenges, we propose a novel framework called Mining Causal Natural Structure (MCNS), which is automatic and domain-agnostic and helps to find the causal natural structures inside time series via the internal causality scheme.
Popularity Debiasing from Exposure to Interaction in Collaborative Filtering
In this paper, we propose a new criterion for popularity debiasing, i. e., in an unbiased recommender system, both popular and unpopular items should receive Interactions Proportional to the number of users who Like it, namely IPL criterion.
Meta Pattern Concern Score: A Novel Evaluation Measure with Human Values for Multi-classifiers
And a case study shows it can not only find the ideal model reducing 0. 53% of dangerous cases by only sacrificing 0. 04% of training accuracy, but also refine the learning rate to train a new model averagely outperforming the original one with a 1. 62% lower value of itself and 0. 36% fewer number of dangerous cases.
TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attack
Recent years have witnessed the success of recurrent neural network (RNN) models in time series classification (TSC).
Towards Powerful Graph Neural Networks: Diversity Matters
For a target node, diverse sampling offers it diverse neighborhoods, i. e., rooted sub-graphs, and the representation of target node is finally obtained via aggregating the representation of diverse neighborhoods obtained using any GNN model.
