106 citations · 151 across the 8 of their papers we have counts for
4 papers · 1 filter
Probabilistic Time Series Forecasting with Structured Shape and Temporal Diversity
Vincent Le Guen, Nicolas Thome
Probabilistic forecasting consists in predicting a distribution of possible future outcomes. In this paper, we address this problem for non-stationary time series, which is very ch…
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models
Vincent Le Guen, Nicolas Thome
This paper addresses the problem of time series forecasting for non-stationary signals and multiple future steps prediction. To handle this challenging task, we introduce DILATE (D…
SHADE: Information Based Regularization for Deep Learning
Michael Blot, Thomas Robert, Nicolas Thome +1
Regularization is a big issue for training deep neural networks. In this paper, we propose a new information-theory-based regularization scheme named SHADE for SHAnnon DEcay. The o…
SHADE: Information-Based Regularization for Deep Learning
Michael Blot, Thomas Robert, Nicolas Thome +1
Regularization is a big issue for training deep neural networks. In this paper, we propose a new information-theory-based regularization scheme named SHADE for SHAnnon DEcay. The o…