no code implementations • 23 May 2024 • Sarah Alnegheimish, Linh Nguyen, Laure Berti-Equille, Kalyan Veeramachaneni
First, we present a prompt-based detection method that directly asks a language model to indicate which elements of the input are anomalies.
no code implementations • 30 Jan 2024 • Lei Xu, Sarah Alnegheimish, Laure Berti-Equille, Alfredo Cuesta-Infante, Kalyan Veeramachaneni
Experimental results on 4 datasets and BERT and distilBERT classifiers show that SP-Defense improves \r{ho} by 14. 6% and 13. 9% and decreases the attack success rate of SP-Attack by 30. 4% and 21. 2% on two classifiers respectively, and decreases the attack success rate of existing attack methods that involve multiple-word perturbations.
no code implementations • 20 Dec 2023 • Alexandra Zytek, Wei-En Wang, Dongyu Liu, Laure Berti-Equille, Kalyan Veeramachaneni
Users in many domains use machine learning (ML) predictions to help them make decisions.
1 code implementation • 26 Oct 2023 • Sarah Alnegheimish, Laure Berti-Equille, Kalyan Veeramachaneni
The framework provides universal abstractions to represent models, extensibility to add new pipelines and datasets, hyperparameter standardization, pipeline verification, and frequent releases with published benchmarks.
no code implementations • 6 Jan 2023 • Laure Berti-Equille, Rafael L. G. Raimundo
Coviability refers to the multiple socio-ecological arrangements and governance structures under which humans and nature can coexist in functional, fair, and persistent ways.
3 code implementations • 27 Dec 2022 • Lawrence Wong, Dongyu Liu, Laure Berti-Equille, Sarah Alnegheimish, Kalyan Veeramachaneni
We then propose AER (Auto-encoder with Regression), a joint model that combines a vanilla auto-encoder and an LSTM regressor to incorporate the successes and address the limitations of each method.
no code implementations • 10 Sep 2022 • Reshmi Ghosh, Michael Craig, H. Scott Matthews, Constantine Samaras, Laure Berti-Equille
Long-term planning of a robust power system requires the understanding of changing demand patterns.
2 code implementations • 19 Apr 2022 • Sarah Alnegheimish, Dongyu Liu, Carles Sala, Laure Berti-Equille, Kalyan Veeramachaneni
The detection of anomalies in time series data is a critical task with many monitoring applications.
no code implementations • 23 Feb 2022 • Alexandra Zytek, Ignacio Arnaldo, Dongyu Liu, Laure Berti-Equille, Kalyan Veeramachaneni
Through extensive experience developing and explaining machine learning (ML) applications for real-world domains, we have learned that ML models are only as interpretable as their features.
1 code implementation • 17 Apr 2021 • Lei Xu, Alfredo Cuesta-Infante, Laure Berti-Equille, Kalyan Veeramachaneni
It improves the quality of adversarial examples by optimizing a critique score which combines the fluency, similarity, and misclassification metrics.
no code implementations • 31 Jul 2019 • Laure Berti-Equille, Ji Meng Loh, Saravanan Thirumuruganathan
In this paper, we introduce the notion of resilience to sampling for outlier detection methods.
2 code implementations • 23 Sep 2014 • Dalia Attia Waguih, Laure Berti-Equille
A fundamental problem in data fusion is to determine the veracity of multi-source data in order to resolve conflicts.
Databases