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Found 4 papers, 3 papers with code
On the Fairness of Disentangled Representations
Recently there has been a significant interest in learning disentangled representations, as they promise increased interpretability, generalization to unseen scenarios and faster learning on downstream tasks.
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs
Stochastic differential equations are an important modeling class in many disciplines.
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems
Parameter inference in ordinary differential equations is an important problem in many applied sciences and in engineering, especially in a data-scarce setting.
MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series Forecasting
In this work, we directly tackle this task with a novel, fully end-to-end deep learning method for time series forecasting.
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