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Found 6 papers, 3 papers with code
NeuralProphet: Explainable Forecasting at Scale
NeuralProphet is a hybrid forecasting framework based on PyTorch and trained with standard deep learning methods, making it easy for developers to extend the framework.
Scalable Data Classification for Security and Privacy
Content based data classification is an open challenge.
AR-Net: A simple Auto-Regressive Neural Network for time-series
In this paper we present a new framework for time-series modeling that combines the best of traditional statistical models and neural networks.
Deep and Confident Prediction for Time Series at Uber
Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing.
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Online Article Ranking as a Constrained, Dynamic, Multi-Objective Optimization Problem
The content ranking problem in a social news website, is typically a function that maximizes a scalar metric of interest like dwell-time.
Rank-to-engage: New Listwise Approaches to Maximize Engagement
For many internet businesses, presenting a given list of items in an order that maximizes a certain metric of interest (e. g., click-through-rate, average engagement time etc.)
