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Found 6 papers, 2 papers with code
ChemTime: Rapid and Early Classification for Multivariate Time Series Classification of Chemical Sensors
To our knowledge, there has yet to be an effort to survey machine learning and time series classification approaches to chemiresistive hardware sensor arrays for the detection of chemical analytes.
ChemVise: Maximizing Out-of-Distribution Chemical Detection with the Novel Application of Zero-Shot Learning
Accurate chemical sensors are vital in medical, military, and home safety applications.
The Pseudo Projection Operator: Applications of Deep Learning to Projection Based Filtering in Non-Trivial Frequency Regimes
Traditional frequency based projection filters, or projection operators (PO), separate signal and noise through a series of transformations which remove frequencies where noise is present.
Neural Network Ensembles: Theory, Training, and the Importance of Explicit Diversity
Ensemble learning is a process by which multiple base learners are strategically generated and combined into one composite learner.
Anomaly Detection via Graphical Lasso
Accordingly, we propose a novel optimization problem that is similar in spirit to Robust Principal Component Analysis (RPCA) and splits the sample covariance matrix $M$ into two parts, $M=F+S$, where $F$ is the cleaned sample covariance whose inverse is sparse and computable by Graphical Lasso, and $S$ contains the outliers in $M$.
Anomaly Detection with Robust Deep Autoencoders
Deep autoencoders, and other deep neural networks, have demonstrated their effectiveness in discovering non-linear features across many problem domains.
