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Found 5 papers, 1 papers with code
Differentiable Physics-based Greenhouse Simulation
We present a differentiable greenhouse simulation model based on physical processes whose parameters can be obtained by training from real data.
Learning predictive representations in autonomous driving to improve deep reinforcement learning
Reinforcement learning using a novel predictive representation is applied to autonomous driving to accomplish the task of driving between lane markings where substantial benefits in performance and generalization are observed on unseen test roads in both simulation and on a real Jackal robot.
Mapless Navigation among Dynamics with Social-safety-awareness: a reinforcement learning approach from 2D laser scans
We observe that our method demonstrates time-efficient path planning behavior with high success rate in mapless navigation tasks.
Robotics
End-to-end Learning of Convolutional Neural Net and Dynamic Programming for Left Ventricle Segmentation
Differentiable programming is able to combine different functions or programs in a processing pipeline with the goal of applying end-to-end learning or optimization.
Generative Adversarial Networks using Adaptive Convolution
Most existing GANs architectures that generate images use transposed convolution or resize-convolution as their upsampling algorithm from lower to higher resolution feature maps in the generator.
