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Found 7 papers, 1 papers with code
Contrastive Credibility Propagation for Reliable Semi-Supervised Learning
CCP unifies semi-supervised learning and noisy label learning for the goal of reliably outperforming a supervised baseline in any data scenario.
Real-time Drift Detection on Time-series Data
Practical machine learning applications involving time series data, such as firewall log analysis to proactively detect anomalous behavior, are concerned with real time analysis of streaming data.
Time Series Anomaly Detection with label-free Model Selection
Our algorithm is easily parallelizable, more robust for ill-conditioned and seasonal data, and highly scalable for a large number of anomaly models.
Log2NS: Enhancing Deep Learning Based Analysis of Logs With Formal to Prevent Survivorship Bias
By combining the strengths of deep learning and symbolic methods, Log2NS provides a very powerful reasoning and debugging tool for log-based data.
Boosted Embeddings for Time Series Forecasting
Many advanced autoregressive methods such as ARIMA were used to develop forecasting models.
One-Shot Induction of Generalized Logical Concepts via Human Guidance
First, we define a distance measure between candidate concept representations that improves the efficiency of search for target concept and generalization.
Structure Learning for Relational Logistic Regression: An Ensemble Approach
We consider the problem of learning Relational Logistic Regression (RLR).
