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Found 11 papers, 5 papers with code
Truth Discovery Algorithms: An Experimental Evaluation
A fundamental problem in data fusion is to determine the veracity of multi-source data in order to resolve conflicts.
Databases
Are Outlier Detection Methods Resilient to Sampling?
In this paper, we introduce the notion of resilience to sampling for outlier detection methods.
R&R: Metric-guided Adversarial Sentence Generation
It improves the quality of adversarial examples by optimizing a critique score which combines the fluency, similarity, and misclassification metrics.
The Need for Interpretable Features: Motivation and Taxonomy
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.
Sintel: A Machine Learning Framework to Extract Insights from Signals
The detection of anomalies in time series data is a critical task with many monitoring applications.
Reconstruction of Long-Term Historical Demand Data
Long-term planning of a robust power system requires the understanding of changing demand patterns.
AER: Auto-Encoder with Regression for Time Series Anomaly Detection
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.
Discovering Transition Pathways Towards Coviability with Machine Learning
Coviability refers to the multiple socio-ecological arrangements and governance structures under which humans and nature can coexist in functional, fair, and persistent ways.
Making the End-User a Priority in Benchmarking: OrionBench for Unsupervised Time Series Anomaly Detection
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.
Pyreal: A Framework for Interpretable ML Explanations
Users in many domains use machine learning (ML) predictions to help them make decisions.
Single Word Change is All You Need: Designing Attacks and Defenses for Text Classifiers
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.
