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Found 7 papers, 4 papers with code
SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakes
Modern deepfake detectors have achieved encouraging results, when training and test images are drawn from the same data collection.
A Meta-level Analysis of Online Anomaly Detectors
The behavior of the detectors is correlated with the characteristics of different datasets (i. e., meta-features), thereby providing a meta-level analysis of their performance.
Knowledge Graph Embedding Methods for Entity Alignment: An Experimental Review
Recently, embedding methods have been used for entity alignment tasks, that learn a vector-space representation of entities which preserves their similarity in the original KGs.
FairER: Entity Resolution with Fairness Constraints
One of the most critical tasks for improving data quality and increasing the reliability of data analytics is Entity Resolution (ER), which aims to identify different descriptions that refer to the same real-world entity.
On Predictive Explanation of Data Anomalies
PROTEUS is designed to return an accurate estimate of out-of-sample predictive performance to serve as a metric of the quality of the approximation.
Matching Web Tables with Knowledge Base Entities: From Entity Lookups to Entity Embeddings
Our results show that: (1) our novel lookup-based method outperforms state-of-the-art lookup-based methods, (2) the semantic embeddings method outperforms lookup-based methods in one benchmark data set, and (3) the lack of a rich schema in Web tables can limit the ability of ontology matching tools in performing high-quality table annotation.
Massively-Parallel Feature Selection for Big Data
We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for feature selection (FS) in Big Data settings (high dimensionality and/or sample size).
