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Found 6 papers, 0 papers with code
Synthetic Data and Simulators for Recommendation Systems: Current State and Future Directions
Synthetic data and simulators have the potential to markedly improve the performance and robustness of recommendation systems.
Unsupervised Distribution Learning for Lunar Surface Anomaly Detection
In this work we show that modern data-driven machine learning techniques can be successfully applied on lunar surface remote sensing data to learn, in an unsupervised way, sufficiently good representations of the data distribution to enable lunar technosignature and anomaly detection.
The Relevance of Bayesian Layer Positioning to Model Uncertainty in Deep Bayesian Active Learning
One of the main challenges of deep learning tools is their inability to capture model uncertainty.
Large-Scale Visual Active Learning with Deep Probabilistic Ensembles
Annotating the right data for training deep neural networks is an important challenge.
Deep Probabilistic Ensembles: Approximate Variational Inference through KL Regularization
In this paper, we introduce Deep Probabilistic Ensembles (DPEs), a scalable technique that uses a regularized ensemble to approximate a deep Bayesian Neural Network (BNN).
How Much Did it Rain? Predicting Real Rainfall Totals Based on Radar Data
We applied a variety of parametric and non-parametric machine learning models to predict the probability distribution of rainfall based on 1M training examples over a single year across several U. S. states.
