Search Results for author: Vincent Le Guen

Found 8 papers, 7 papers with code

Complementing Brightness Constancy with Deep Networks for Optical Flow Prediction

1 code implementation8 Jul 2022 Vincent Le Guen, Clément Rambour, Nicolas Thome

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.

Optical Flow Estimation

Deep Time Series Forecasting with Shape and Temporal Criteria

1 code implementation9 Apr 2021 Vincent Le Guen, Nicolas Thome

This paper addresses the problem of multi-step time series forecasting for non-stationary signals that can present sudden changes.

Change Detection Dynamic Time Warping +1

Probabilistic Time Series Forecasting with Shape and Temporal Diversity

1 code implementation NeurIPS 2020 Vincent Le Guen, Nicolas Thome

We introduce the STRIPE model for representing structured diversity based on shape and time features, ensuring both probable predictions while being sharp and accurate.

Point Processes Probabilistic Time Series Forecasting

Probabilistic Time Series Forecasting with Structured Shape and Temporal Diversity

1 code implementation14 Oct 2020 Vincent Le Guen, Nicolas Thome

We introduce the STRIPE model for representing structured diversity based on shape and time features, ensuring both probable predictions while being sharp and accurate.

Point Processes Probabilistic Time Series Forecasting

Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting

2 code implementations ICLR 2021 Yuan Yin, Vincent Le Guen, Jérémie Dona, Emmanuel de Bézenac, Ibrahim Ayed, Nicolas Thome, Patrick Gallinari

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

2 code implementations CVPR 2020 Vincent Le Guen, Nicolas Thome

Leveraging physical knowledge described by partial differential equations (PDEs) is an appealing way to improve unsupervised video prediction methods.

Disentanglement Video Prediction +1

Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models

3 code implementations NeurIPS 2019 Vincent Le Guen, Nicolas Thome

We introduce a differentiable loss function suitable for training deep neural nets, and provide a custom back-prop implementation for speeding up optimization.

Change Detection Dynamic Time Warping +2

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