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Found 5 papers, 2 papers with code
Continuous-Time Video Generation via Learning Motion Dynamics with Neural ODE
In order to perform unconditional video generation, we must learn the distribution of the real-world videos.
Vid-ODE: Continuous-Time Video Generation with Neural Ordinary Differential Equation
Video generation models often operate under the assumption of fixed frame rates, which leads to suboptimal performance when it comes to handling flexible frame rates (e. g., increasing the frame rate of the more dynamic portion of the video as well as handling missing video frames).
Multi-Image Super-Resolution for Remote Sensing using Deep Recurrent Networks
High-resolution satellite imagery is critical for various earth observation applications related to environment monitoring, geoscience, forecasting, and land use analysis.
Deep-dust: Predicting concentrations of fine dust in Seoul using LSTM
Polluting fine dusts in South Korea which are mainly consisted of biomass burning and fugitive dust blown from dust belt is significant problem these days.
Reliable and Explainable Machine Learning Methods for Accelerated Material Discovery
We also propose a transfer learning technique and show that the performance loss due to models' simplicity can be overcome by exploiting correlations among different material properties.
