Search Results for author:
Found 7 papers, 5 papers with code
Representation learning of writing style
We evaluate the model on multiple datasets, on both the authorship clustering and the authorship attribution tasks.
Making Parametric Anomaly Detection on Tabular Data Non-Parametric Again
Deep learning for tabular data has garnered increasing attention in recent years, yet employing deep models for structured data remains challenging.
Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments
This observation challenges the potential benefits of ensemble methods to combine supervised, and AD approaches to enhance performance.
Benchmarking Robustness of Deep Reinforcement Learning approaches to Online Portfolio Management
Deep Reinforcement Learning approaches to Online Portfolio Selection have grown in popularity in recent years.
Beyond Individual Input for Deep Anomaly Detection on Tabular Data
To the best of our knowledge, this is the first work to successfully combine feature-feature and sample-sample dependencies for anomaly detection on tabular datasets.
TracInAD: Measuring Influence for Anomaly Detection
As with many other tasks, neural networks prove very effective for anomaly detection purposes.
DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills
In this article, we first frame the research problem of optimizing an adaptive and personalized spaced repetition scheduler when memorization concerns the application of underlying multiple skills.
