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Found 8 papers, 7 papers with code
Complementing Brightness Constancy with Deep Networks for Optical Flow Prediction
Since BC is an approximate physical model violated in several situations, we propose to train a physically-constrained network complemented with a data-driven network.
Deep learning for spatio-temporal forecasting -- application to solar energy
This thesis tackles the subject of spatio-temporal forecasting with deep learning.
Deep Time Series Forecasting with Shape and Temporal Criteria
This paper addresses the problem of multi-step time series forecasting for non-stationary signals that can present sudden changes.
Probabilistic Time Series Forecasting with Shape and Temporal Diversity
We introduce the STRIPE model for representing structured diversity based on shape and time features, ensuring both probable predictions while being sharp and accurate.
Probabilistic Time Series Forecasting with Structured Shape and Temporal Diversity
We introduce the STRIPE model for representing structured diversity based on shape and time features, ensuring both probable predictions while being sharp and accurate.
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
In this work, we introduce the APHYNITY framework, a principled approach for augmenting incomplete physical dynamics described by differential equations with deep data-driven models.
Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction
Leveraging physical knowledge described by partial differential equations (PDEs) is an appealing way to improve unsupervised video prediction methods.
Ranked #2 on
Video Prediction
on SynpickVP
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models
We introduce a differentiable loss function suitable for training deep neural nets, and provide a custom back-prop implementation for speeding up optimization.
