1 code implementation • 30 Jun 2022 • Marius Dragoi, Elena Burceanu, Emanuela Haller, Andrei Manolache, Florin Brad
Analyzing the distribution shift of data is a growing research direction in nowadays Machine Learning (ML), leading to emerging new benchmarks that focus on providing a suitable scenario for studying the generalization properties of ML models.
Ranked #5 on Unsupervised Anomaly Detection on AnoShift
1 code implementation • NAACL 2021 • Andrei Manolache, Florin Brad, Elena Burceanu
Leveraging deep learning models for Anomaly Detection (AD) has seen widespread use in recent years due to superior performances over traditional methods.
Ranked #1 on Anomaly Detection on AG News
1 code implementation • 2 Nov 2023 • Ioana Pintilie, Andrei Manolache, Florin Brad
Our models outperform the baselines on synthetic datasets and are competitive on real-world datasets, illustrating the potential of diffusion-based methods for AD in multivariate time series.
1 code implementation • 9 Dec 2021 • Florin Brad, Andrei Manolache, Elena Burceanu, Antonio Barbalau, Radu Ionescu, Marius Popescu
One of the main drivers of the recent advances in authorship verification is the PAN large-scale authorship dataset.
1 code implementation • 3 Oct 2023 • Chendi Qian, Andrei Manolache, Kareem Ahmed, Zhe Zeng, Guy Van Den Broeck, Mathias Niepert, Christopher Morris
Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structured input.
no code implementations • 7 Jul 2022 • Andrei Manolache, Florin Brad, Antonio Barbalau, Radu Tudor Ionescu, Marius Popescu
The DarkWeb represents a hotbed for illicit activity, where users communicate on different market forums in order to exchange goods and services.
no code implementations • 14 Dec 2023 • Andrei Manolache
Deep anomaly detection methods have become increasingly popular in recent years, with methods like Stacked Autoencoders, Variational Autoencoders, and Generative Adversarial Networks greatly improving the state-of-the-art.