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Found 8 papers, 3 papers with code
SoftED: Metrics for Soft Evaluation of Time Series Event Detection
They improved event detection evaluation by associating events and their representative detections, incorporating temporal tolerance in over 36\% of experiments compared to the usual classification metrics.
A Simplicity Bubble Problem in Formal-Theoretic Learning Systems
When mining large datasets in order to predict new data, limitations of the principles behind statistical machine learning pose a serious challenge not only to the Big Data deluge, but also to the traditional assumptions that data generating processes are biased toward low algorithmic complexity.
Hyperspherical embedding for novel class classification
However, this traditional approach is not useful for identifying classes unseen on the training set, known as the open set problem.
Estimation of COVID-19 under-reporting in Brazilian States through SARI
Therefore, the objective of this work is to estimate the under-reporting of cases and deaths of COVID-19 in Brazilian states using data from the Infogripe on notification of Severe Acute Respiratory Infection (SARI).
Applications Computers and Society
DJEnsemble: On the Selection of a Disjoint Ensemble of Deep Learning Black-Box Spatio-Temporal Models
In the online part, we compute a DJEnsemble plan which minimizes a multivariate cost function based on estimates for the prediction error and the execution cost -- producing a model spatial allocation matrix -- and run the optimal ensemble plan.
STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for Weather Forecasting
Applying machine learning models to meteorological data brings many opportunities to the Geosciences field, such as predicting future weather conditions more accurately.
Parallel Computation of PDFs on Big Spatial Data Using Spark
We consider big spatial data, which is typically produced in scientific areas such as geological or seismic interpretation.
A note on the complexity of the causal ordering problem
In this note we provide a concise report on the complexity of the causal ordering problem, originally introduced by Simon to reason about causal dependencies implicit in systems of mathematical equations.
