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Found 5 papers, 2 papers with code
Local and Global Trend Bayesian Exponential Smoothing Models
This paper describes a family of seasonal and non-seasonal time series models that can be viewed as generalisations of additive and multiplicative exponential smoothing models, to model series that grow faster than linear but slower than exponential.
Brain Model State Space Reconstruction Using an LSTM Neural Network
Approach An LSTM filter was trained on simulated EEG data generated by a neural mass model using a wide range of parameters.
SETAR-Tree: A Novel and Accurate Tree Algorithm for Global Time Series Forecasting
On the other hand, in the forecasting community, general-purpose tree-based regression algorithms (forests, gradient-boosting) have become popular recently due to their ease of use and accuracy.
A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping
In this paper we present Sourcerer, a Bayesian-inspired, deep learning-based, semi-supervised DA technique for producing land cover maps from SITS data.
Understanding Knowledge Gaps in Visual Question Answering: Implications for Gap Identification and Testing
After identifying KGs for each question, we examine the skew in the distribution of questions for each KG.
